We explore the potential and practical challenges in the use of artificial intelligence (AI) in cyber risk analytics, for improv- ing organisational resilience and understanding cyber risk. The research is focused on identifying the role of AI in con- nected devices such as Internet of Things (IoT) devices. Through literature review, we identify wide ranging and creative methodologies for cyber analytics and explore the risks of deliberately influencing or disrupting behaviours to socio- technical systems. This resulted in the modelling of the connections and interdependencies between a system’s edge components to both external and internal services and systems. We focus on proposals for models, infrastructures and frameworks of IoT systems found in both business reports and technical papers. We analyse this juxtaposition of related systems and technologies, in academic and industry papers published in the past 10 years. Then, we report the results of a qualitative empirical study that correlates the academic literature with key technological advances in connected devices. The work is based on grouping future and present techniques and presenting the results through a new con- ceptual framework. With the application of social science’s grounded theory, the framework details a new process for a prototype of AI-enabled dynamic cyber risk analytics at the edge.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
In 2006, Cyber Physical Systems (CPS), the new word was invented in the United States [1]. The combination of devices like sensors with embedded systems is quickly receiving its place in cyber world. These devices jointly with the information filed are becoming the main focal point, called as Cyber Physical Systems. This word was found keeping in mind the escaling significance of relations among the mutually related computing systems with the physical world [2]. The author of this paper gives an overview of CPS architecture, its functions and its security threat.
Citation: Navin Dhinnesh ADC, Mepco Schlenk Engineering College. "Cyber Physical System." Global Research and Development Journal For Engineering 34 2018: 12 - 14.
Cyber risk at the edge: current and future trends on cyber risk analytics and...Petar Radanliev
Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.
Artificial Intelligence and the Internet of Things in Industry 4.0Petar Radanliev
This paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspec- tive. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic clas- sifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.
EFFECTIVE MALWARE DETECTION APPROACH BASED ON DEEP LEARNING IN CYBER-PHYSICAL...ijcsit
Cyber-physical Systems based on advanced networks interact with other networks through wireless
communication to enhance interoperability, dynamic mobility, and data supportability. The vast data is
managed through a cloud platform, vulnerable to cyber-attacks. It will threaten the customers in terms of
privacy and security as third-party users should authenticate the network. If it fails, it will create extensive
damage and threat to the established network and makes the hacker malfunction the network services
efficiently. This paper proposes a DL-based CPS approach to identify and mitigate the malware cyberphysical system attack of Denial of Service (DoS) and Distributed Denial of Service (DDoS) as it ensures
adequate decision support. At the same time, the trusted user nodes are connected to the network. It helps
to improve the privacy and authentication of the network by improving the data accuracy and Quality of
Service (QoS) in the network. Here the analysis is determined on the proposed system to improve the
network reliability and security compared to some of the existing SVM-based and Apriori-based detection
approaches.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
In 2006, Cyber Physical Systems (CPS), the new word was invented in the United States [1]. The combination of devices like sensors with embedded systems is quickly receiving its place in cyber world. These devices jointly with the information filed are becoming the main focal point, called as Cyber Physical Systems. This word was found keeping in mind the escaling significance of relations among the mutually related computing systems with the physical world [2]. The author of this paper gives an overview of CPS architecture, its functions and its security threat.
Citation: Navin Dhinnesh ADC, Mepco Schlenk Engineering College. "Cyber Physical System." Global Research and Development Journal For Engineering 34 2018: 12 - 14.
Cyber risk at the edge: current and future trends on cyber risk analytics and...Petar Radanliev
Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.
Artificial Intelligence and the Internet of Things in Industry 4.0Petar Radanliev
This paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspec- tive. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic clas- sifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.
EFFECTIVE MALWARE DETECTION APPROACH BASED ON DEEP LEARNING IN CYBER-PHYSICAL...ijcsit
Cyber-physical Systems based on advanced networks interact with other networks through wireless
communication to enhance interoperability, dynamic mobility, and data supportability. The vast data is
managed through a cloud platform, vulnerable to cyber-attacks. It will threaten the customers in terms of
privacy and security as third-party users should authenticate the network. If it fails, it will create extensive
damage and threat to the established network and makes the hacker malfunction the network services
efficiently. This paper proposes a DL-based CPS approach to identify and mitigate the malware cyberphysical system attack of Denial of Service (DoS) and Distributed Denial of Service (DDoS) as it ensures
adequate decision support. At the same time, the trusted user nodes are connected to the network. It helps
to improve the privacy and authentication of the network by improving the data accuracy and Quality of
Service (QoS) in the network. Here the analysis is determined on the proposed system to improve the
network reliability and security compared to some of the existing SVM-based and Apriori-based detection
approaches.
Cyber-physical Systems based on advanced networks interact with other networks through wireless
communication to enhance interoperability, dynamic mobility, and data supportability. The vast data is
managed through a cloud platform, vulnerable to cyber-attacks. It will threaten the customers in terms of
privacy and security as third-party users should authenticate the network. If it fails, it will create extensive
damage and threat to the established network and makes the hacker malfunction the network services
efficiently. This paper proposes a DL-based CPS approach to identify and mitigate the malware cyber-
physical system attack of Denial of Service (DoS) and Distributed Denial of Service (DDoS) as it ensures
adequate decision support. At the same time, the trusted user nodes are connected to the network. It helps
to improve the privacy and authentication of the network by improving the data accuracy and Quality of
Service (QoS) in the network. Here the analysis is determined on the proposed system to improve the
network reliability and security compared to some of the existing SVM-based and Apriori-based detection
approaches.
https://jst.org.in/index.html
Our journal has digital transformation, effective management strategies are crucial. Our pages unfold discussions on navigating the complexities of modern business landscapes, strategic decision-making, and adaptive leadership—essential elements for success in the 21st century.
Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective ...Sebastiano Panichella
Lecture entitled "Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective Test Generation and Selection" at the International Summer School
on Search- and Machine Learning-based Software Engineering
June 22-24, 2022 - Córdoba, Spain
Sebastiano Panichella and Christian Birchler
Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective ...Sebastiano Panichella
Lecture entitled "Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective Test Generation and Selection" at the International Summer School
on Search- and Machine Learning-based Software Engineering
June 22-24, 2022 - Córdoba, Spain
Sebastiano Panichella and Christian Birchler
SECURITY AND PRIVACY AWARE PROGRAMMING MODEL FOR IOT APPLICATIONS IN CLOUD EN...ijccsa
The introduction of Internet of Things (IoT) applications into daily life has raised serious privacy concerns
among consumers, network service providers, device manufacturers, and other parties involved. This paper
gives a high-level overview of the three phases of data collecting, transmission, and storage in IoT systems
as well as current privacy-preserving technologies. The following elements were investigated during these
three phases:(1) Physical and data connection layer security mechanisms(2) Network remedies(3)
Techniques for distributing and storing data. Real-world systems frequently have multiple phases and
incorporate a variety of methods to guarantee privacy. Therefore, for IoT research, design, development,
and operation, having a thorough understanding of all phases and their technologies can be beneficial. In
this Study introduced two independent methodologies namely generic differential privacy (GenDP) and
Cluster-Based Differential privacy ( Cluster-based DP) algorithms for handling metadata as intents and
intent scope to maintain privacy and security of IoT data in cloud environments. With its help, we can
virtual and connect enormous numbers of devices, get a clearer understanding of the IoT architecture, and
store data eternally. However, due of the dynamic nature of the environment, the diversity of devices, the
ad hoc requirements of multiple stakeholders, and hardware or network failures, it is a very challenging
task to create security-, privacy-, safety-, and quality-aware Internet of Things apps. It is becoming more
and more important to improve data privacy and security through appropriate data acquisition. The
proposed approach resulted in reduced loss performance as compared to Support Vector Machine (SVM) ,
Random Forest (RF) .
Novel authentication framework for securing communication in internet-of-things IJECEIAES
Internet-of-Things (IoT) offers a big boon towards a massive network of connected devices and is considered to offer coverage to an exponential number of the smart appliance in the very near future. Owing to the nascent stage of evolution of IoT, it is shrouded by security loopholes because of various reasons. Review of existing research-based solution highlights the usage of conventional cryptographic-based solution over the traditional mechanism of data forwarding process between IoT nodes and gateway. The proposed system presents a novel solution to this problem by a model that is capable of performing a highly secured and cost-effective authentication process. The proposed system introduces Authentication Using Signature (AUS) as well as Security with Complexity Reduction (SCR) for the purpose to resist participation of any form of unknown threats. The outcome of the model shows better security strength with faster response time and energy saving of the IoT nodes.
DEVELOPMENT OF A CONCEPTUAL MODEL OF ADAPTIVE ACCESS RIGHTS MANAGEMENT WITH U...IAEME Publication
The paper describes the conceptual model of adaptive control of cyber protection
of the informatization object (IO). Petri's Networks were used as a mathematical
device to solve the problem of adaptive control of user access rights. The simulation
model is proposed and the simulation in PIPE v4.3.0 package is performed. The
possibility of automating the procedures for adjusting the user profile to minimize or
neutralize cyber threats in the objects of informatization is shown. The model of
distribution of user tasks in computer networks of IO is proposed. The model, unlike
the existing, is based on the mathematical apparatus of Petri's Networks and contains
variables that allow reducing the power of the state space. Access control method
(ACM) is added. The addenda touched upon aspects of reconciliation of access rights
that are requested by the task and requirements of the security policy and the degree
of consistency of tasks and access to the IO nodes. Adjustment of rules and security
metrics for new tasks or redistributable tasks is described in the notation of Petri nets
Hyperparameters optimization XGBoost for network intrusion detection using CS...IAESIJAI
With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learning algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CICIDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.
Titles with Abstracts_2023-2024_Cyber Security.pdfinfo751436
Implementing a cybersecurity project can offer numerous advantages for organizations in today's digitally connected world. Here are some key benefits:
Protection against Cyber Threats: The primary goal of cybersecurity projects is to safeguard an organization's digital assets from various cyber threats such as malware, ransomware, phishing attacks, and more. This protection is crucial for maintaining the integrity, confidentiality, and availability of sensitive information.
Data Privacy Compliance: Many industries have specific regulations and compliance requirements regarding the protection of sensitive data. Implementing cybersecurity measures helps organizations adhere to these regulations, avoiding legal consequences and potential financial penalties.
Business Continuity: Cybersecurity projects often include strategies for disaster recovery and business continuity planning. In the event of a cyberattack or data breach, having a robust cybersecurity infrastructure in place can minimize downtime and ensure that critical business operations continue without significant disruption.
Risk Management: Cybersecurity projects help organizations identify, assess, and manage potential risks associated with their digital assets. This proactive approach allows businesses to make informed decisions about risk mitigation and prioritize resources effectively.
Customer Trust and Reputation: A strong cybersecurity posture can enhance customer trust and protect the reputation of an organization. Customers are more likely to engage with businesses that prioritize the security of their information, leading to increased brand loyalty.
Intellectual Property Protection: For many organizations, intellectual property (IP) is a valuable asset. Cybersecurity measures help protect intellectual property from theft or unauthorized access, ensuring that companies can maintain a competitive edge in the market.
Employee Awareness and Training: Cybersecurity projects often include employee training programs to raise awareness about cybersecurity threats and best practices. Well-informed employees are a crucial line of defense against social engineering attacks and other cyber threats.
Cost Savings: While implementing cybersecurity measures involves an initial investment, it can result in long-term cost savings. The financial impact of a data breach or cyberattack, including potential legal fees, reputation damage, and loss of business, can far exceed the cost of preventive cybersecurity measures.
Cyber Insurance Benefits: Having a robust cybersecurity infrastructure in place may make an organization more eligible for favorable terms and rates on cyber insurance policies, providing an additional layer of financial protection.
Adaptability to Emerging Threats: Cybersecurity projects are dynamic and adaptive, allowing organizations to stay ahead of evolving cyber threats.
Artificial intelligence in cyber physical systemsPetar Radanliev
The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution is inevitable and autonomous because of the increased integration of connected devices (IoT) in cyber physical systems. To support this argument, taxonomic methodol- ogy is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework.
Detecting network attacks model based on a convolutional neural network IJECEIAES
Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
Cyber physical systems: A smart city perspective IJECEIAES
Cyber-physical system (CPS) is a terminology used to describe multiple systems of existing infrastructure and manufacturing system that combines computing technologies (cyber space) into the physical space to integrate human interaction. This paper does a literature review of the work related to CPS in terms of its importance in today’s world. Further, this paper also looks at the importance of CPS and its relationship with internet of things (IoT). CPS is a very broad area and is used in variety of fields and some of these major fields are evaluated. Additionally, the implementation of CPS and IoT is major enabler for smart cities and various examples of such implementation in the context of Dubai and UAE are researched. Finally, security issues related to CPS in general are also reviewed.
How Cyber-Physical Systems Are Reshaping the Robotics LandscapeCognizant
The rapid growth of analytics, AI and related intelligent software is merely the first phase of the robotics revolution. Computer algorithms that learn and improve the output of systems over time are now managing and controlling physical systems in ways that enable machines to function autonomously.
Analysis of Energy Management Scheme in Smart City: A Reviewijtsrd
A brilliant city misuses feasible data and correspondence innovations to improve the quality and the presentation of urban administrations for natives and government, while decreasing assets utilization. Wise vitality control in structures is a significant viewpoint in this. The Internet of Things can give an answer. It means to associate various heterogeneous gadgets through the web, for which it needs an adaptable layered design where the things, the general population and the cloud administrations are consolidated to encourage an application task. Such adaptable IoT various leveled engineering model will be presented in this paper with a review of each key segment for astute vitality control in structures for keen urban communities. Manisha Kumari Singh | Prof. Avinash Sharma "Analysis of Energy Management Scheme in Smart City: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29446.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/29446/analysis-of-energy-management-scheme-in-smart-city-a-review/manisha-kumari-singh
Top 10 Cited Network Security Research Articles 2021 - 2022IJNSA Journal
The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
Enhancements in the world of digital forensicsIAESIJAI
Currently, the rapid advancement of computer systems and mobile phones has resulted in their utilization in unlawful acts. Ensuring adequate and effective security measures poses a difficult task due to the intricate nature of these devices, thereby exacerbating the challenges associated with investigating crimes involving them. Digital forensics, which involves investigating cyber crimes, plays a crucial role in this realm. Extensive research has been conducted in this field to aid forensic investigations in addressing contemporary obstacles. This paper aims to explore the progress made in the applications of digital forensics and security, encompassing various aspects, and provide insights into the evolution of digital forensics over the past five years.
Ubiquitous devices are rising in popularity and sophistication. Internet of Things (IoT) avails opportunities for devices with powerful sensing, computing and interaction capabilities ranging from smartphones, wearable devices, home appliances, transport sensors and health products to share information through the internet. Due to vast data shared and increased interaction; they have attracted the interest of malware writers. Internet of Things environments poses unique challenges such as device latency, scalability, lack of antimalware tools and heterogeneity of device architectures that makes malware synthesis complex. In this paper we review literature on internet of things malware categories, support technologies, propagation and tools
Artificial Intelligence and Quantum CryptographyPetar Radanliev
Dr Petar Radanliev
Department of Computer Sciences
University of Oxford
Abstract:
The technological advancements made in recent times, particularly in Artificial Intelligence (AI) and Quantum Computing, have brought about significant changes in technology. These advancements have profoundly impacted quantum cryptography, a field where AI methodologies hold tremendous potential to enhance the efficiency and robustness of cryptographic systems. However, the emergence of quantum computers has created a new challenge for existing security algorithms, commonly called the 'quantum threat'. Despite these challenges, there are promising avenues for integrating neural network-based AI in cryptography, which has significant implications for future digital security paradigms. This summary highlights the key themes in the intersection of AI and quantum cryptography, including the potential benefits of AI-driven cryptography, the challenges that need to be addressed, and the prospects of this interdisciplinary research area.
Keywords: Artificial Intelligence, Quantum Algorithms, Neural Networks, Quantum-AI Integration, Quantum Threats, AI-enhanced Security, Quantum Information Processing.
Artificial Intelligence and Quantum CryptographyPetar Radanliev
Abstract:
The technological advancements made in recent times, particularly in Artificial Intelligence (AI) and Quantum Computing, have brought about significant changes in technology. These advancements have profoundly impacted quantum cryptography, a field where AI methodologies hold tremendous potential to enhance the efficiency and robustness of cryptographic systems. However, the emergence of quantum computers has created a new challenge for existing security algorithms, commonly called the 'quantum threat'. Despite these challenges, there are promising avenues for integrating neural network-based AI in cryptography, which has significant implications for future digital security paradigms. This summary highlights the key themes in the intersection of AI and quantum cryptography, including the potential benefits of AI-driven cryptography, the challenges that need to be addressed, and the prospects of this interdisciplinary research area.
Keywords: Artificial Intelligence, Quantum Algorithms, Neural Networks, Quantum-AI Integration, Quantum Threats, AI-enhanced Security, Quantum Information Processing.
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Cyber-physical Systems based on advanced networks interact with other networks through wireless
communication to enhance interoperability, dynamic mobility, and data supportability. The vast data is
managed through a cloud platform, vulnerable to cyber-attacks. It will threaten the customers in terms of
privacy and security as third-party users should authenticate the network. If it fails, it will create extensive
damage and threat to the established network and makes the hacker malfunction the network services
efficiently. This paper proposes a DL-based CPS approach to identify and mitigate the malware cyber-
physical system attack of Denial of Service (DoS) and Distributed Denial of Service (DDoS) as it ensures
adequate decision support. At the same time, the trusted user nodes are connected to the network. It helps
to improve the privacy and authentication of the network by improving the data accuracy and Quality of
Service (QoS) in the network. Here the analysis is determined on the proposed system to improve the
network reliability and security compared to some of the existing SVM-based and Apriori-based detection
approaches.
https://jst.org.in/index.html
Our journal has digital transformation, effective management strategies are crucial. Our pages unfold discussions on navigating the complexities of modern business landscapes, strategic decision-making, and adaptive leadership—essential elements for success in the 21st century.
Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective ...Sebastiano Panichella
Lecture entitled "Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective Test Generation and Selection" at the International Summer School
on Search- and Machine Learning-based Software Engineering
June 22-24, 2022 - Córdoba, Spain
Sebastiano Panichella and Christian Birchler
Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective ...Sebastiano Panichella
Lecture entitled "Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective Test Generation and Selection" at the International Summer School
on Search- and Machine Learning-based Software Engineering
June 22-24, 2022 - Córdoba, Spain
Sebastiano Panichella and Christian Birchler
SECURITY AND PRIVACY AWARE PROGRAMMING MODEL FOR IOT APPLICATIONS IN CLOUD EN...ijccsa
The introduction of Internet of Things (IoT) applications into daily life has raised serious privacy concerns
among consumers, network service providers, device manufacturers, and other parties involved. This paper
gives a high-level overview of the three phases of data collecting, transmission, and storage in IoT systems
as well as current privacy-preserving technologies. The following elements were investigated during these
three phases:(1) Physical and data connection layer security mechanisms(2) Network remedies(3)
Techniques for distributing and storing data. Real-world systems frequently have multiple phases and
incorporate a variety of methods to guarantee privacy. Therefore, for IoT research, design, development,
and operation, having a thorough understanding of all phases and their technologies can be beneficial. In
this Study introduced two independent methodologies namely generic differential privacy (GenDP) and
Cluster-Based Differential privacy ( Cluster-based DP) algorithms for handling metadata as intents and
intent scope to maintain privacy and security of IoT data in cloud environments. With its help, we can
virtual and connect enormous numbers of devices, get a clearer understanding of the IoT architecture, and
store data eternally. However, due of the dynamic nature of the environment, the diversity of devices, the
ad hoc requirements of multiple stakeholders, and hardware or network failures, it is a very challenging
task to create security-, privacy-, safety-, and quality-aware Internet of Things apps. It is becoming more
and more important to improve data privacy and security through appropriate data acquisition. The
proposed approach resulted in reduced loss performance as compared to Support Vector Machine (SVM) ,
Random Forest (RF) .
Novel authentication framework for securing communication in internet-of-things IJECEIAES
Internet-of-Things (IoT) offers a big boon towards a massive network of connected devices and is considered to offer coverage to an exponential number of the smart appliance in the very near future. Owing to the nascent stage of evolution of IoT, it is shrouded by security loopholes because of various reasons. Review of existing research-based solution highlights the usage of conventional cryptographic-based solution over the traditional mechanism of data forwarding process between IoT nodes and gateway. The proposed system presents a novel solution to this problem by a model that is capable of performing a highly secured and cost-effective authentication process. The proposed system introduces Authentication Using Signature (AUS) as well as Security with Complexity Reduction (SCR) for the purpose to resist participation of any form of unknown threats. The outcome of the model shows better security strength with faster response time and energy saving of the IoT nodes.
DEVELOPMENT OF A CONCEPTUAL MODEL OF ADAPTIVE ACCESS RIGHTS MANAGEMENT WITH U...IAEME Publication
The paper describes the conceptual model of adaptive control of cyber protection
of the informatization object (IO). Petri's Networks were used as a mathematical
device to solve the problem of adaptive control of user access rights. The simulation
model is proposed and the simulation in PIPE v4.3.0 package is performed. The
possibility of automating the procedures for adjusting the user profile to minimize or
neutralize cyber threats in the objects of informatization is shown. The model of
distribution of user tasks in computer networks of IO is proposed. The model, unlike
the existing, is based on the mathematical apparatus of Petri's Networks and contains
variables that allow reducing the power of the state space. Access control method
(ACM) is added. The addenda touched upon aspects of reconciliation of access rights
that are requested by the task and requirements of the security policy and the degree
of consistency of tasks and access to the IO nodes. Adjustment of rules and security
metrics for new tasks or redistributable tasks is described in the notation of Petri nets
Hyperparameters optimization XGBoost for network intrusion detection using CS...IAESIJAI
With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learning algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CICIDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.
Titles with Abstracts_2023-2024_Cyber Security.pdfinfo751436
Implementing a cybersecurity project can offer numerous advantages for organizations in today's digitally connected world. Here are some key benefits:
Protection against Cyber Threats: The primary goal of cybersecurity projects is to safeguard an organization's digital assets from various cyber threats such as malware, ransomware, phishing attacks, and more. This protection is crucial for maintaining the integrity, confidentiality, and availability of sensitive information.
Data Privacy Compliance: Many industries have specific regulations and compliance requirements regarding the protection of sensitive data. Implementing cybersecurity measures helps organizations adhere to these regulations, avoiding legal consequences and potential financial penalties.
Business Continuity: Cybersecurity projects often include strategies for disaster recovery and business continuity planning. In the event of a cyberattack or data breach, having a robust cybersecurity infrastructure in place can minimize downtime and ensure that critical business operations continue without significant disruption.
Risk Management: Cybersecurity projects help organizations identify, assess, and manage potential risks associated with their digital assets. This proactive approach allows businesses to make informed decisions about risk mitigation and prioritize resources effectively.
Customer Trust and Reputation: A strong cybersecurity posture can enhance customer trust and protect the reputation of an organization. Customers are more likely to engage with businesses that prioritize the security of their information, leading to increased brand loyalty.
Intellectual Property Protection: For many organizations, intellectual property (IP) is a valuable asset. Cybersecurity measures help protect intellectual property from theft or unauthorized access, ensuring that companies can maintain a competitive edge in the market.
Employee Awareness and Training: Cybersecurity projects often include employee training programs to raise awareness about cybersecurity threats and best practices. Well-informed employees are a crucial line of defense against social engineering attacks and other cyber threats.
Cost Savings: While implementing cybersecurity measures involves an initial investment, it can result in long-term cost savings. The financial impact of a data breach or cyberattack, including potential legal fees, reputation damage, and loss of business, can far exceed the cost of preventive cybersecurity measures.
Cyber Insurance Benefits: Having a robust cybersecurity infrastructure in place may make an organization more eligible for favorable terms and rates on cyber insurance policies, providing an additional layer of financial protection.
Adaptability to Emerging Threats: Cybersecurity projects are dynamic and adaptive, allowing organizations to stay ahead of evolving cyber threats.
Artificial intelligence in cyber physical systemsPetar Radanliev
The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution is inevitable and autonomous because of the increased integration of connected devices (IoT) in cyber physical systems. To support this argument, taxonomic methodol- ogy is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework.
Detecting network attacks model based on a convolutional neural network IJECEIAES
Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
Cyber physical systems: A smart city perspective IJECEIAES
Cyber-physical system (CPS) is a terminology used to describe multiple systems of existing infrastructure and manufacturing system that combines computing technologies (cyber space) into the physical space to integrate human interaction. This paper does a literature review of the work related to CPS in terms of its importance in today’s world. Further, this paper also looks at the importance of CPS and its relationship with internet of things (IoT). CPS is a very broad area and is used in variety of fields and some of these major fields are evaluated. Additionally, the implementation of CPS and IoT is major enabler for smart cities and various examples of such implementation in the context of Dubai and UAE are researched. Finally, security issues related to CPS in general are also reviewed.
How Cyber-Physical Systems Are Reshaping the Robotics LandscapeCognizant
The rapid growth of analytics, AI and related intelligent software is merely the first phase of the robotics revolution. Computer algorithms that learn and improve the output of systems over time are now managing and controlling physical systems in ways that enable machines to function autonomously.
Analysis of Energy Management Scheme in Smart City: A Reviewijtsrd
A brilliant city misuses feasible data and correspondence innovations to improve the quality and the presentation of urban administrations for natives and government, while decreasing assets utilization. Wise vitality control in structures is a significant viewpoint in this. The Internet of Things can give an answer. It means to associate various heterogeneous gadgets through the web, for which it needs an adaptable layered design where the things, the general population and the cloud administrations are consolidated to encourage an application task. Such adaptable IoT various leveled engineering model will be presented in this paper with a review of each key segment for astute vitality control in structures for keen urban communities. Manisha Kumari Singh | Prof. Avinash Sharma "Analysis of Energy Management Scheme in Smart City: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29446.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/29446/analysis-of-energy-management-scheme-in-smart-city-a-review/manisha-kumari-singh
Top 10 Cited Network Security Research Articles 2021 - 2022IJNSA Journal
The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
Enhancements in the world of digital forensicsIAESIJAI
Currently, the rapid advancement of computer systems and mobile phones has resulted in their utilization in unlawful acts. Ensuring adequate and effective security measures poses a difficult task due to the intricate nature of these devices, thereby exacerbating the challenges associated with investigating crimes involving them. Digital forensics, which involves investigating cyber crimes, plays a crucial role in this realm. Extensive research has been conducted in this field to aid forensic investigations in addressing contemporary obstacles. This paper aims to explore the progress made in the applications of digital forensics and security, encompassing various aspects, and provide insights into the evolution of digital forensics over the past five years.
Ubiquitous devices are rising in popularity and sophistication. Internet of Things (IoT) avails opportunities for devices with powerful sensing, computing and interaction capabilities ranging from smartphones, wearable devices, home appliances, transport sensors and health products to share information through the internet. Due to vast data shared and increased interaction; they have attracted the interest of malware writers. Internet of Things environments poses unique challenges such as device latency, scalability, lack of antimalware tools and heterogeneity of device architectures that makes malware synthesis complex. In this paper we review literature on internet of things malware categories, support technologies, propagation and tools
Similar to Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge (20)
Artificial Intelligence and Quantum CryptographyPetar Radanliev
Dr Petar Radanliev
Department of Computer Sciences
University of Oxford
Abstract:
The technological advancements made in recent times, particularly in Artificial Intelligence (AI) and Quantum Computing, have brought about significant changes in technology. These advancements have profoundly impacted quantum cryptography, a field where AI methodologies hold tremendous potential to enhance the efficiency and robustness of cryptographic systems. However, the emergence of quantum computers has created a new challenge for existing security algorithms, commonly called the 'quantum threat'. Despite these challenges, there are promising avenues for integrating neural network-based AI in cryptography, which has significant implications for future digital security paradigms. This summary highlights the key themes in the intersection of AI and quantum cryptography, including the potential benefits of AI-driven cryptography, the challenges that need to be addressed, and the prospects of this interdisciplinary research area.
Keywords: Artificial Intelligence, Quantum Algorithms, Neural Networks, Quantum-AI Integration, Quantum Threats, AI-enhanced Security, Quantum Information Processing.
Artificial Intelligence and Quantum CryptographyPetar Radanliev
Abstract:
The technological advancements made in recent times, particularly in Artificial Intelligence (AI) and Quantum Computing, have brought about significant changes in technology. These advancements have profoundly impacted quantum cryptography, a field where AI methodologies hold tremendous potential to enhance the efficiency and robustness of cryptographic systems. However, the emergence of quantum computers has created a new challenge for existing security algorithms, commonly called the 'quantum threat'. Despite these challenges, there are promising avenues for integrating neural network-based AI in cryptography, which has significant implications for future digital security paradigms. This summary highlights the key themes in the intersection of AI and quantum cryptography, including the potential benefits of AI-driven cryptography, the challenges that need to be addressed, and the prospects of this interdisciplinary research area.
Keywords: Artificial Intelligence, Quantum Algorithms, Neural Networks, Quantum-AI Integration, Quantum Threats, AI-enhanced Security, Quantum Information Processing.
Cyber Diplomacy: Defining the Opportunities for Cybersecurity and Risks from Artificial Intelligence, IoT, Blockchains, and Quantum Computing
Abstract: Cyber diplomacy is critical in dealing with the digital era's evolving cybersecurity dangers and possibilities. This article investigates the impact of Artificial Intelligence (AI), the Internet of Things (IoT), Blockchains, and Quantum Computing on cyber diplomacy. AI holds the potential for proactive threat identification and response, while IoT enables international information sharing. Blockchains enable secure data sharing and document verification, but they also pose new threats, such as AI-driven cyber-attacks, IoT privacy breaches, blockchain vulnerabilities, and the potential for quantum computing to break encryption. This article conducts case study reviews in combination with secondary data analysis and emphasises the value of international cooperation in developing global norms and frameworks to control responsible technology adoption. Cyber diplomacy can promote cybersecurity, protect national interests, and foster mutual trust among nations in the digital sphere by capitalising on possibilities and reducing threats.
PhD Thesis:
Blockchain Cybersecurity
Dr Petar Radanliev
University of Oxford
PhD Thesis:
"Blockchain Cybersecurity: A Comprehensive Study"
Dr Petar Radanliev
University of Oxford
Abstract:
This thesis presents an exhaustive exploration of the interplay between blockchain technology and cybersecurity. It delves into how blockchain can revolutionise cybersecurity practices, addressing existing challenges and opening up new avenues for secure digital interactions. The study provides a thorough analysis of blockchain's inherent security features, such as decentralisation, immutability, and transparency, and how these attributes contribute to enhancing cybersecurity across various domains. Additionally, the thesis examines potential vulnerabilities within blockchain systems and proposes strategies for mitigating these risks. By combining theoretical insights with practical case studies, this work aims to offer a holistic view of blockchain's role in shaping the future landscape of cybersecurity.
Chapter 1: Introduction
Overview of Blockchain Technology
Cybersecurity Challenges in the Digital Age
Objectives and Scope of the Study
Chapter 2: Fundamentals of Blockchain Technology
History and Evolution of Blockchain
Key Components and Functioning of Blockchain Systems
Types of Blockchain: Public, Private, and Consortium
Chapter 3: Blockchain in Cybersecurity
Decentralisation as a Security Feature
Immutability and Data Integrity
Transparency and Trust in Blockchain Systems
Chapter 4: Blockchain Applications in Cybersecurity
Use Cases in Various Industries
Blockchain in Identity Management and Authentication
Secure Transactions and Smart Contracts
Chapter 5: Vulnerabilities and Risks in Blockchain
Analysis of Known Blockchain Vulnerabilities
Potential Attack Vectors and Their Implications
Risk Mitigation Strategies and Best Practices
Chapter 6: Future Trends and Challenges
Emerging Trends in Blockchain and Cybersecurity
Scalability, Interoperability, and Regulatory Challenges
Future Research Directions
Chapter 7: Conclusion
Summary of Key Findings
Contributions to the Field of Blockchain Cybersecurity
Recommendations for Future Research and Practice
Appendices
Technical Details of Blockchain Protocols
Case Studies and Practical Examples
Bibliography
Comprehensive List of Academic References and Key Sources
This thesis contributes to the existing body of knowledge by providing a detailed analysis of blockchain's potential and limitations in the realm of cybersecurity, offering valuable insights for academics, industry practitioners, and policy makers.
I started my career testing security in the military and defence industries. Then, I moved into managing cyber risks in the finance world. After ten years in these fields, I returned to academics, earning my PhD, Master's, and Bachelor's degrees.
My postdoctoral work took me to several universities: Imperial College London, the University of Cambridge, MIT, and back to the University of Oxford
PhD Thesis:
Blockchain Cybersecurity
Dr Petar Radanliev
University of Oxford
PhD Thesis:
"Blockchain Cybersecurity: A Comprehensive Study"
Dr Petar Radanliev
University of Oxford
Abstract:
This thesis presents an exhaustive exploration of the interplay between blockchain technology and cybersecurity. It delves into how blockchain can revolutionise cybersecurity practices, addressing existing challenges and opening up new avenues for secure digital interactions. The study provides a thorough analysis of blockchain's inherent security features, such as decentralisation, immutability, and transparency, and how these attributes contribute to enhancing cybersecurity across various domains. Additionally, the thesis examines potential vulnerabilities within blockchain systems and proposes strategies for mitigating these risks. By combining theoretical insights with practical case studies, this work aims to offer a holistic view of blockchain's role in shaping the future landscape of cybersecurity.
Chapter 1: Introduction
Overview of Blockchain Technology
Cybersecurity Challenges in the Digital Age
Objectives and Scope of the Study
Chapter 2: Fundamentals of Blockchain Technology
History and Evolution of Blockchain
Key Components and Functioning of Blockchain Systems
Types of Blockchain: Public, Private, and Consortium
Chapter 3: Blockchain in Cybersecurity
Decentralisation as a Security Feature
Immutability and Data Integrity
Transparency and Trust in Blockchain Systems
Chapter 4: Blockchain Applications in Cybersecurity
Use Cases in Various Industries
Blockchain in Identity Management and Authentication
Secure Transactions and Smart Contracts
Chapter 5: Vulnerabilities and Risks in Blockchain
Analysis of Known Blockchain Vulnerabilities
Potential Attack Vectors and Their Implications
Risk Mitigation Strategies and Best Practices
Chapter 6: Future Trends and Challenges
Emerging Trends in Blockchain and Cybersecurity
Scalability, Interoperability, and Regulatory Challenges
Future Research Directions
Chapter 7: Conclusion
Summary of Key Findings
Contributions to the Field of Blockchain Cybersecurity
Recommendations for Future Research and Practice
Appendices
Technical Details of Blockchain Protocols
Case Studies and Practical Examples
Bibliography
Comprehensive List of Academic References and Key Sources
This thesis contributes to the existing body of knowledge by providing a detailed analysis of blockchain's potential and limitations in the realm of cybersecurity, offering valuable insights for academics, industry practitioners, and policy makers.
I started my career testing security in the military and defence industries. Then, I moved into managing cyber risks in the finance world. After ten years in these fields, I returned to academics, earning my PhD, Master's, and Bachelor's degrees.
My postdoctoral work took me to several universities: Imperial College London, the University of Cambridge, MIT, and back to the University of Oxford
The Rise and Fall of Cryptocurrencies: Defining the Economic and Social Values of Blockchain Technologies, assessing the Opportunities, and defining the Financial and Cybersecurity Risks of the Metaverse.
Ethics and Responsible AI Deployment
Abstract: As Artificial Intelligence (AI) becomes more prevalent, protecting personal privacy is a critical ethical issue that must be addressed. This article explores the need for ethical AI systems that safeguard individual privacy while complying with ethical standards. By taking a multidisciplinary approach, the research examines innovative algorithmic techniques such as differential privacy, homomorphic encryption, federated learning, international regulatory frameworks, and ethical guidelines. The study concludes that these algorithms effectively enhance privacy protection while balancing the utility of AI with the need to protect personal data. The article emphasises the importance of a comprehensive approach that combines technological innovation with ethical and regulatory strategies to harness the power of AI in a way that respects and protects individual privacy.
Artificial intelligence (AI) has the potential to significantly impact employment, social equity, and economic systems in ways that require careful ethical analysis and aggressive legislative measures to mitigate negative consequences. This means that the implications of AI in different industries, such as healthcare, finance, and transportation, must be carefully considered.
Due to the global nature of AI technology, global collaboration must be fostered to establish standards and regulatory frameworks that transcend national boundaries. This includes the establishment of ethical guidelines that AI researchers and developers worldwide should follow.
To address emergent ethical concerns with AI, future research must focus on several recommendations. Firstly, ethical considerations must be integrated into the design phase of AI systems and not treated as an afterthought. This is known as "Ethics by Design" and involves incorporating ethical standards during the development phase of AI systems to ensure that the technology aligns with ethical principles.
Secondly, interdisciplinary research that combines AI, ethics, law, social science, and other relevant domains should be promoted to produce well-rounded solutions to ethical dilemmas. This requires the participation of experts from different fields to identify and address ethical issues.
Thirdly, regulatory frameworks must be dynamic and adaptive to keep pace with the rapid evolution of AI technologies. This means that regulatory frameworks must be flexible enough to accommodate changes in AI technology while ensuring ethical standards are maintained.
Fourthly, empirical research should be conducted to understand the real-world implications of AI systems on individuals and society, which can then inform ethical principles and policies. This means that empirical data must be collected to understand how AI affects people in different contexts.
Finally, risk assessment procedures should be improved to better analyse the ethical hazards associated with AI applications.
Artificial Intelligence: Survey of Cybersecurity Capabilities, Ethical Concer...Petar Radanliev
The comprehensive survey articulates the multifaceted dimensions of Artificial Intelligence (AI), spanning its historical roots, advancements, and ethical dilemmas. It starts by tracing the intellectual lineage of AI to ancient mythology and proceeds to discuss the revolutionary contributions of Generative Pre-trained Transformers (GPT), particularly GPT-4, in problem-solving and real-world applications. The paper also delves into the darker applications of AI, including its role in cyberattacks and automated phishing. Various techniques of adversarial attacks that undermine AI systems, such as Fast Gradient Sign Method (FGSM), Jacobian-based Saliency Map Attack (JSMA), and Universal Adversarial Perturbations (UAP), are meticulously examined. The paper further expounds on Membership Inference Attacks (MIA), a significant privacy concern, and presents various strategies to defend against adversarial attacks. A global perspective on AI regulations, encompassing UK, New Zealand, the EU, and China policies, is also provided. It culminates in weighing the ethical considerations against the security risks in AI, contextualised by global crime statistics. This survey serves as an exhaustive resource for understanding AI's complexity, capabilities, and ethical implications, offering invaluable insights for researchers, policymakers, and industry experts.
Artificial Intelligence and Quantum Cryptography: A comprehensive analysis of...Petar Radanliev
The technological advancements made in recent times, particularly in Artificial Intelligence (AI) and quantum computing, have brought about significant changes in technology. These advancements have profoundly impacted quantum cryptography, a field where AI methodologies hold tremendous potential to enhance the efficiency and robustness of cryptographic systems. However, the emergence of quantum computers has created a new challenge for existing security algorithms, commonly called the 'quantum threat'. Despite these challenges, there are promising avenues for integrating neural network-based AI in cryptography, which has significant implications for future digital security paradigms. This summary highlights the key themes in the intersection of AI and quantum cryptography, including the potential benefits of AI-driven cryptography, the challenges that need to be addressed, and the future prospects of this interdisciplinary research area.
Red Teaming Generative AI and Quantum CryptographyPetar Radanliev
In the contemporary digital age, Quantum Computing and Artificial Intelligence (AI) convergence is reshaping the cyber landscape, introducing both unprecedented opportunities and potential vulnerabilities.
This research, conducted over five years, delves into the cybersecurity implications of this convergence, with a particular focus on AI/Natural Language Processing (NLP) models and quantum cryptographic protocols, notably the BB84 method and specific NIST-approved algorithms. Utilising Python and C++ as primary computational tools, the study employs a "red teaming" approach, simulating potential cyber-attacks to assess the robustness of quantum security measures. Preliminary research over 12 months laid the groundwork, which this study seeks to expand upon, aiming to translate theoretical insights into actionable, real-world cybersecurity solutions. Located at the University of Oxford's technology precinct, the research benefits from state-of-the-art infrastructure and a rich collaborative environment. The study's overarching goal is to ensure that as the digital world transitions to quantum-enhanced operations, it remains resilient against AI-driven cyber threats. The research aims to foster a safer, quantum-ready digital future through iterative testing, feedback integration, and continuous improvement. The findings are intended for broad dissemination, ensuring that the knowledge benefits academia and the global community, emphasising the responsible and secure harnessing of quantum technology.
1. Introduction: Quantum Technology, AI, and the Evolving Cybersecurity Landscape
In the contemporary technological epoch, the rapid evolution of Quantum Computing and Artificial Intelligence (AI) is reshaping our digital realm, expanding the cyber risk horizon. As we stand on the cusp of a quantum revolution, the cyber-attack surface undergoes a transformation, heralding a future rife with potential cyber threats.
2. Theoretical Underpinning
This research endeavours to construct a robust cybersecurity framework, ensuring AI's harmonious and secure integration with the Quantum Internet. Central to our exploration is evaluating AI/Natural Language Processing (NLP) models and their interaction with quintessential quantum security protocols, notably the BB84 method and select NIST-endorsed algorithms. Leveraging the computational prowess of Python and C++, we aim to critically assess the resilience of these quantum security paradigms by simulating AI-driven cyber-attacks.
3. Research Objectives
Envision a quantum-enhanced internet, operating at unparalleled speeds, yet fortified against AI-mediated cyber threats. This vision encapsulates our primary objective: to ensure that the digital advancements of the future, powered by AI, remain benevolent and secure. Over a five-year trajectory, our mission is to harness AI's potential in a manner that is beneficial and safeguarded against malevolent exploits.
Red Teaming AI and Quantum
In the contemporary digital age, Quantum Computing and Artificial Intelligence (AI) convergence is reshaping the cyber landscape, introducing both unprecedented opportunities and potential vulnerabilities.
This research, conducted over five years, delves into the cybersecurity implications of this convergence, with a particular focus on AI/Natural Language Processing (NLP) models and quantum cryptographic protocols, notably the BB84 method and specific NIST-approved algorithms. Utilising Python and C++ as primary computational tools, the study employs a "red teaming" approach, simulating potential cyber-attacks to assess the robustness of quantum security measures. Preliminary research over 12 months laid the groundwork, which this study seeks to expand upon, aiming to translate theoretical insights into actionable, real-world cybersecurity solutions. Located at the University of Oxford's technology precinct, the research benefits from state-of-the-art infrastructure and a rich collaborative environment. The study's overarching goal is to ensure that as the digital world transitions to quantum-enhanced operations, it remains resilient against AI-driven cyber threats. The research aims to foster a safer, quantum-ready digital future through iterative testing, feedback integration, and continuous improvement. The findings are intended for broad dissemination, ensuring that the knowledge benefits academia and the global community, emphasising the responsible and secure harnessing of quantum technology.
-- Introduction: Quantum Technology, AI, and the Evolving Cybersecurity Landscape
In the contemporary technological epoch, the rapid evolution of Quantum Computing and Artificial Intelligence (AI) is reshaping our digital realm, expanding the cyber risk horizon. As we stand on the cusp of a quantum revolution, the cyber-attack surface transforms, heralding a future rife with potential cyber threats.
-- Theoretical Underpinning
This research endeavours to construct a robust cybersecurity framework, ensuring AI's harmonious and secure integration with the Quantum Internet. Central to our exploration is evaluating AI/Natural Language Processing (NLP) models and their interaction with quintessential quantum security protocols, notably the BB84 method and select NIST-endorsed algorithms. Leveraging the computational prowess of Python and C++, we aim to critically assess the resilience of these quantum security paradigms by simulating AI-driven cyber-attacks.
-- Research Objectives
Envision a quantum-enhanced internet, operating at unparalleled speeds yet fortified against AI-mediated cyber threats. This vision encapsulates our primary objective: to ensure that the digital advancements of the future, powered by AI, remain benevolent and secure. Over a five-year trajectory, our mission is to harness AI's potential in a manner that is beneficial and safeguarded against malevolent exploits.
Red Teaming Generative AI/NLP, the BB84 quantum cryptography protocol and the...Petar Radanliev
In the contemporary digital age, Quantum Computing and Artificial Intelligence (AI) convergence is reshaping the cyber landscape, introducing both unprecedented opportunities and potential vulnerabilities.
This research, conducted over five years, delves into the cybersecurity implications of this convergence, with a particular focus on AI/Natural Language Processing (NLP) models and quantum cryptographic protocols, notably the BB84 method and specific NIST-approved algorithms. Utilising Python and C++ as primary computational tools, the study employs a "red teaming" approach, simulating potential cyber-attacks to assess the robustness of quantum security measures. Preliminary research over 12 months laid the groundwork, which this study seeks to expand upon, aiming to translate theoretical insights into actionable, real-world cybersecurity solutions. Located at the University of Oxford's technology precinct, the research benefits from state-of-the-art infrastructure and a rich collaborative environment. The study's overarching goal is to ensure that as the digital world transitions to quantum-enhanced operations, it remains resilient against AI-driven cyber threats. The research aims to foster a safer, quantum-ready digital future through iterative testing, feedback integration, and continuous improvement. The findings are intended for broad dissemination, ensuring that the knowledge benefits academia and the global community, emphasising the responsible and secure harnessing of quantum technology.
Cyber Diplomacy: Defining the Opportunities for Cybersecurity and Risks from Artificial Intelligence, IoT, Blockchains, and Quantum Computing
-- One of the main benefits of cyber intelligence sharing is the access to shared threat intelligence
Sharing threat intelligence on time allows for a faster and more effective reaction to cyber incidents, limiting the potential impact and minimising damage
Cyber threat intelligence sharing encourages a collaborative approach to cybersecurity, boosting collective defence efforts among organisations and nations
Sharing threat intelligence allows organisations to learn from each other's experiences, resulting in skill growth and enhanced knowledge in cybersecurity
Sharing cyber threat intelligence supports public-private cooperation, combining the skills and resources of both sectors to effectively tackle cyber threats
-- Cyber threat intelligence frequently originates in a variety of formats and patterns, making it challenging to consolidate and analyse data across several organisations efficiently.
-- CISCP is a United States government effort that promotes information sharing between federal agencies and private-sector organisations in order to improve cybersecurity
One ongoing academic effort is the Global Cyber Security Capacity Centre at the University of Oxford
GCSCC is a cybersecurity capacity-building centre, advocating an increase in the global scale, pace, quality, and impact of cybersecurity capacity-building activities.
-- Overcoming geopolitical tensions in cyber discussions is a difficult and delicate endeavour, but it is critical for developing international collaboration and effectively combating cyber threats
-- Diplomatic efforts should be directed towards identifying common ground and areas of mutual interest in cybersecurity
-- Creating avenues for regular communication and discussion can help nations create trust and understanding
-- Cyber diplomacy needs to be focused on encouraging joint research initiatives, cyber threat information exchange, and collaborative efforts to strengthen cybersecurity capabilities to build bridges and foster collaboration
Nations can collaborate to develop rules that improve cybersecurity while discouraging malevolent behaviour.
-- Several future developments are anticipated to affect the landscape of cyber diplomacy as the field of cybersecurity evolves
These developments will have a substantial impact on international cooperation, policy, and responses to growing cyber threats
One of the anticipated future trends is the emergence of international cyber norms
The creation of internationally recognised cyber norms will gain traction
Nations will work more closely together to develop common principles and standards guiding responsible state behaviour in cyberspace
Nations will need to address concerns such as AI ethics, the possible threats of autonomous cyber systems, and the development of rules for the appropriate use of AI in cyber operations.
Dance Movement Therapy in the Metaverse: A Fusion of Virtual Rhythms and Real Healing
In the vast expanse of the digital universe, where pixels and avatars reign supreme, there lies an unexpected sanctuary of healing: dance. The metaverse, a realm of virtual reality (VR), augmented reality (AR), and mixed reality (MR), is not just a playground for gamers and tech enthusiasts. It's emerging as a therapeutic space where the age-old art of dance is being reimagined. As our physical and digital worlds intertwine, dance in the metaverse is not only a testament to the evolution of art but also a beacon of hope for those grappling with mental health challenges. This immersive dance movement therapy, blending the boundaries of the real and virtual, offers not just an exhilarating physical exercise but also a transformative journey for the mind. Dive with us into this rhythmic odyssey, where every move is a step towards wellness.
Dance Movement Therapy in the Metaverse: A New Frontier for Mental HealthPetar Radanliev
-- Problem Background: Mental health issues, especially anxiety and depression, are rising globally. We need non-pharmacological interventions. This brings into light the potential of integrating alternative therapies in extended reality environments, such as the Metaverse.
-- Data Collection: Utilised wearable sensors to gather data on participants’ movements, physiological responses, and emotional feedback.
Methodology | AI and ML models: DeepDance model uses a combination of CNNs and RNNs to learn the temporal and spatial patterns of dance movements. The DeepDance model has been shown to be effective in classifying different types of dance movements, as well as in predicting the outcome of a dance performance.
-- Experimental approach: AI and ML models: Time Series Analysis
-- Key Findings: Dance Movement Therapy in extended reality environments shows potential as a beneficial alternative therapy.
Software Bill of Materials and the Vulnerability Exploitability eXchange Petar Radanliev
The UK and the U.S. are in a special relationship that requires compliance with cybersecurity regulations and cyber solid diplomacy. The Executive Order 14028 which imposes a compulsory requirement for Software Bill of Materials (SBOM), has exposed the need for deeper collaboration between the UK and the U.S. cybersecurity agencies.
We need a comprehensive cyber policy that prioritises cybersecurity as a top national priority for the UK. The UK and the U.S. have individually developed their forward-looking cybersecurity strategy to protect their critical infrastructure, businesses, and citizens from evolving cyber risks. The UK has fallen behind in following the U.S. requirements for Software Bill of Materials (SBOM) and cyber vulnerabilities. This exposes a gap in the UK and the U.S. cyber diplomacy and requires a new strategy that builds on existing collaborative efforts and shared expertise in countering cyber threats.
To bring the UK back on track with compliance with standards, legislations, and regulations in the U.S. and to strengthen the UK and the U.S. collective defence capabilities, the new strategy must prioritise improving information sharing, intelligence collaboration and collaborative cybersecurity exercises. This is particularly relevant and important in light of the difficulties SBOMs present in assuring software supply chain security.
This necessitates active participation in multilateral forums that advance cyber policy and advance global norms for cyberspace while also encouraging responsible state behaviour and addressing vulnerabilities in a coordinated fashion. The UK and the U.S. need to set the standard for promoting cyber resilience by creating a secure digital future not only for the UK and the U.S. but through coordinated efforts. The new strategy must also provide opportunities for engagement with the larger international community. The first step in doing this is to address the complexities of managing SBOMs and cyber vulnerabilities with the guiding principles of transparency, cooperation, and international stability in cyberspace.
When the level of cooperation and collaboration has been re-established, the problem of managing the vast volume of new vulnerabilities will be imposed on UK cybersecurity professionals. This study is designed to identify the solutions that would reduce the burden on U.S. cybersecurity professionals today, and the workloads on UK cybersecurity professionals in the future.
The solutions investigated in this study are based on using Generative Pre-Trained Transformers, Natural Language Processing, Artificial Intelligence, and other Machine Learning algorithms in Software Vulnerability Management. The objective of the study is to identify how such tools can be used for automations in the Software Bill of Materials (SBOM) and the Vulnerability-Exploitability eXchange (VEX).
The Rise and Fall of Cryptocurrencies: Defining the Economic and Social Value...Petar Radanliev
This paper contextualises the common queries of "why is crypto crashing?" and "why is crypto down?", the research transcends beyond the frequent market fluctuations to unravel how cryptocurrencies fundamentally work and the step-by-step process on how to create a cryptocurrency.
The Rise and Fall of Cryptocurrencies: Defining the Economic and Social Value...Petar Radanliev
The study examines blockchain technologies and their pivotal role in the evolving Metaverse, shedding light on topics such as how to invest in cryptocurrency, the mechanics behind crypto mining, and strategies to effectively buy and trade cryptocurrencies. Through an interdisciplinary approach, the research transitions from the fundamental principles of fintech investment strategies to the overarching implications of blockchain within the Metaverse. Alongside exploring machine learning potentials in financial sectors and risk assessment methodologies, the study critically assesses whether developed or developing nations are poised to reap greater benefits from these technologies. Moreover, it probes into both enduring and dubious crypto projects, drawing a distinct line between genuine blockchain applications and Ponzi-like schemes. The conclusion resolutely affirms the continuing dominance of blockchain technologies, underlined by a profound exploration of their intrinsic value and a reflective commentary by the author on the potential risks confCybersecurity Risks ronting individual investors.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
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In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
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JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
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Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
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State of global ICS asset and network exposure
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Major cyber events in 2024
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Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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6], real-time enabled CPS platforms and automated CPSs
that guide skilled workers in production environments [7].
In this context, we investigate how such systems enable
artificial intelligence (AI) advances in real-time process-
ing, sensing and actuation between these new systems
and provide capabilities for system analysis of the cyber
structures involved [8].We therefore focus here on artificial
intelligence, representing a concept that consolidates the
cyber-physical and social aspects of the risks in which new
technology is deployed [9].
The objective of this study was to build upon exist-
ing work on cyber risk standardisation [10], and AI in CPS
[11], but with a greater focus on exploring the potential
and practical challenges in the use of AI, in the service
of improving personal and organisational resilience. The
methodology applied in the study follows recommen-
dations in existing studies on adaptive risk models [12];
feedback in IoT systems [13]; in layered IoT architecture
[14]; and for optimising decision-making [15]. We identi-
fied approaches to model the risk within complex inter-
connected and coupled systems in cyber-physical envi-
ronments. This involved modelling the connections and
interdependencies between components to both external
and internal services and systems. In modelling the con-
nections and interdependencies, we studied CPSs that
demonstrate the use and application of IoT technology.
The research reported here has two research objectives.
Firstly, we present an up to date overview of existing and
emerging advancements in the field of cyber risk analytics.
This combines the existing literature to derive common
basic terminology and approaches and to incorporate
existing standards into a new feedback mechanism for
risk analytics. Secondly, we capture the best practices and
provoke a debate among practitioners and academics by
offering a new understanding of network cyber risk and
the role of AI in future CPS. This architecture is developed
throughout the paper and can serve as a best practice and
inform initial steps taken for design and prototype of AI-
enabled dynamic cyber risk analytics.
2
Literature review on artificial intelligence,
CPS and predictive cyber risk analytics
CPSs and IoT produce a vast amount of data, and the anal-
ysis of such big data requires advanced analytical tools.
For clearing up the noise and inconsistency of the data,
we almost certainly require AI-enhanced analytical tools
[16]. In terms of data streams, the IoT has been described
as a revolutionary technology enhancement that changes
traditional life into a high tech lifestyle [17]. CPS architec-
tures on the other hand represent a very broad concept
[18]. A system must integrate these diverse concepts
into a cognitive state for big data analytics and statistical
machine learning to predict cyber risks [19]. But the design
of big data systems for edge computing environments is
challenging [20].
One of the most pressing points for CPS is perhaps secu-
rity [21], both electronic and physical, that relates physical
and cyber systems [22]. Such security requires information
assurance and protection for data in transit from physical
and electronic domains and storage facilities [23]. In addi-
tion, asset management and access control are required
for granting or denying requests to information and pro-
cessing services [24], especially because CPS will interface
with nontechnical users and because influence across
administrative boundaries is possible [25]. Techniques
are needed to address novel vulnerabilities caused by life
cycle issues including diminishing manufacturing sources
and the update of assets [26]. These include approaches
for engineering system dynamics across multiple time-
scales [27], like loosely time-triggered architectures [28]
and structure dynamics control [29].
Furthermore, CPS requires anti-counterfeit and supply
chain risk management to counteract malicious supply
chain components that have been modified from their
original design to cause disruption or unauthorised func-
tion [30]. Along with standardisation of design and process
[31], hyper-connectivity in the digital supply chain [32]
also needs to be supported. It is suggested that limiting
source code access to crucial and skilled personnel can
provide software assurance and application security and
may be necessary for eliminating the introduction of delib-
erate flaws and vulnerabilities in CPSs [33].
Security measures should include forensics, prognostics
and recovery plans, for the analysis of cyber-attacks and
for co-ordination with other CPSs and entities that identify
external cyber-attack vectors. To address this, an internal
track and trace network process can assist in detecting or
preventing the existence of weaknesses in the logistics
security controls [34]. To support this, a process for anti-
malicious and anti-tamper system engineering is needed
to prevent the exploitation of CPS vulnerabilities identified
through reverse engineering attacks [35].
2.1 Taxonomy of focus areas for artificial
intelligence for CPS risk analytics
The Smart literature review framework based on latent
Dirichlet allocation [36] was used to perform a taxonomic
analysis. The resulting areas of focus are presented in a
taxonomy with abbreviations (Table 1) that support the
robust integration of artificial intelligence with existing
CPS architecture systems [37]. The taxonomy presents
the areas of focus identified in the literature on cyber risk
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analytics [38], where cyber and physical components and
connectors constitute the entire system at runtime [39].
The areas of focus (AoF) in Table 1 emphasise the need
for privacy in the feedback mechanism for cyber-attack
reporting and shared databases in CPS risk analytics. In
the following section, systematic analysis is applied to
each focal area to determine its overlap with the litera-
ture on artificial intelligence in CPS predictive cyber risk
analytics.
3
Artificial intelligence for manufacturing
and‘servitization’
‘Servitization’ is a move from selling physical products
to selling the ongoing services that those products per-
form or the ongoing services that support a products’
operation. In the context of artificial intelligence in CPS risk
analytics these services include predictive maintenance,
the forecasting of machine failure and the automatic diag-
nosis of failures. For example, intelligent machine-learning
algorithms take information from industrial IoT sensors
and platforms in order to automatically diagnose failures
and estimate the remaining useful life of machinery.
3.1 Grounded theory for taxonomies design
Here, we are applying the grounded theory (GT) method
to group the requirements for artificial intelligence for
CPS risk analytics for ‘servitization’ in manufacturing. The
grounded theory analysis is built into a conceptual dia-
gram in Fig. 1, representing the cascading hierarchical
process through the areas of focus for CPS security.
Figure 1 is a tool for visualising the areas of focus derived
from the analysis. The areas of focus in Fig. 1 are latter clas-
sified in the five levels of artificial intelligence in CPS (see
Table 3).
3.1.1 Electronic and physical security for artificial
technologies—EaPS
Thisrequiresreal-timedataacquisitionandstoragesolutions
[40] for fleets of machines [41], providing adaptive analysis
and peer-to-peer monitoring.
3.1.2 Information assurance and data security for artificial
technologies—ISaDS
This needs to be supported with autonomous cognitive
decisions, machine-learning algorithms and high-perfor-
mance computing or data analysis [42], supported with fast
cyber-attack information sharing and reporting via shared
database resources.
3.1.3 Asset management and access control for cyber risk
analytics—AMaAC
In dynamic cyber risk analytics, this requires that machines
evolve into CPS [43].
3.1.4 Life cycle and anti‑counterfeit for artificial
intelligence for cyber risk analytics—SCRM
This needs task-specific human machine interfaces [44], for
self-awaremachinesandcomponentprognosticsandhealth
management [45].
3.1.5 Diminishing manufacturing sources, material
shortages and supply chain risk management—LCM
This is required for prioritising and optimising decisions with
self-optimising production systems [46], supported with
production-planning computer visualisation, such as SCADA
systems integration with virtual reality [47] for developing
the decision support system.
Table 1 Taxonomy of areas of focus (AoF) for cognitive feedback
mechanism in predictive cyber risk analytics
Taxonomy of focus areas for artificial intelligence for CPS risk
analytics—Glossary of acronyms 2
CPS security CPSS
Areas of focus AoF
5C architecture 5C
Electronic and physical security EaPS
Information assurance and data security ISaDS
Asset management and access control AMaAC
Life cycle and anti-counterfeit LCM
Diminishing manufacturing sources, material shortages
and supply chain risk management
SCRM
Software assurance and application security SAAS
Forensics, prognostics and recovery plans FRP
Track and trace TaT
Anti-malicious and anti-tamper AMAT
Fig. 1 CPS security in the areas of focus in five levels of CPSs
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3.1.6 Software assurance and application security
for artificial cognition—SAAS
This requires a big data platform [48, 49] for sensors con-
dition-based monitoring. Such platforms can enable com-
plex models, such as cyber city designs [50] using struc-
tured communications for mobile CPS [51], cross-domain
end-to-end communication among objects and cloud
computing techniques.
3.1.7 Forensics, prognostics and recovery plans for artificial
cognition—FPR
This needs to be informed by key performance indicators
[52].
3.1.8 Track and trace in cyber risk analytics—TaT
Feedback and control mechanisms are required for ena-
bling supervisory control of actions, to avoid or grant
required access or to design a resilient control system [53].
3.1.9 Anti‑malicious and anti‑tamper—AMAT
This would be facilitated with loosely time-triggered archi-
tectures [54] and structure dynamics control.
3.2 Taxonomy of requirements for artificial
intelligence for CPS in manufacturing
and‘servitization’
The requirements for AI for CPS in manufacturing and
‘servitization’are presented in a taxonomy with abbrevia-
tions (Table 1) that support a robust integration of artificial
intelligence for the cyber risk analytics.The taxonomy pre-
sents the requirements for AI identified in the literature on
predictive cyber risk analytics, where AI components and
connectors service the entire system at runtime.
The taxonomy of requirements in Table 2 for artificial
intelligence for CPS in manufacturing and ‘servitization’,
enables a holistic understanding of the requirements for
integrating cognitive CPS in the cyber risk analytics with
dynamic real-time data from manufacturing and‘servitiza-
tion’.The grouping of requirements is used in the following
section to analyse the required applications and technolo-
gies and to build a cascading architecture for integrating
artificial intelligence for CPS. This topic was identified as
imperative in the engineering literature [53], for assessing
the impact of IoT cyber risks.
4
Design and prototype of AI‑enabled
dynamic cyber risk analytics at the edge
From applying the grounded theory to design a taxon-
omy of future requirements, a new design emerges for the
Table 2 Taxonomy of requirements for artificial intelligence for CPS
in manufacturing and‘servitization’
Self-maintaining connection
Software assurance and application security
Big data platform BDP
Mobile CPS mCPS
Required:
Condition-based monitoring CBM
Self-aware conversion
Life cycle and anti-counterfeit
Task specific human machine interfaces HMI
Self-aware machines and components MaC
Anti-malicious and anti-tamper
Loosely time-triggered architectures LTTA
Structure dynamics control SDC
Required:
Prognostics and health management PHM
Cyber self-compare
Electronic and physical security
Real-time data acquisition and storage solutions RTD
Fleet of machines FoM
Adaptive analysis AA
Peer-to-peer monitoring PtPM
Required:
Cyber-physical systems CPS
Self-predicting cognition
Diminishing manufacturing sources, material shortages and sup-
ply chain risk management
Prioritising and optimising decisions POD
Self-optimising production systems SOPS
Information assurance and data security
Autonomous cognitive decisions ACD
Machine-learning algorithms MLA
High-performance computing for data analysis HPC
Information sharing and reporting ISR
Required:
Decision support system DSS
Self-organising and self-configuring
Track and trace
Supervisory control of actions to avoid or grant access CoA
Forensics, prognostics and recovery plans
Key performance indicators KPI
Asset management and access control
Cyber-physical production systems CPPS
Required:
Resilient control system RCS
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future role of AI in CPS, which includes (1) self-maintaining
machine connections for acquiring data and selecting sen-
sors; (2) self-awareness algorithms for the conversion of
data into information; (3) connecting machines to create
self-comparing cyber networks that can predict future
machine behaviour; (4) the capacity to generate cognitive
knowledge of the system to self-predict and self-optimise
before transferring knowledge to the user; and (5) a con-
figuration feedback and supervisory control route from
cyberspace to physical space, that allows machines to self-
configure, self-organise and to be self-adaptive.
The emerging applications and technologies in Table 3
are presented in the form of a cascading framework in
Fig. 2 to hierarchically organise their relationships in arti-
ficial intelligence for CPS. Grounded theory is applied to
identify the hierarchy of order as identified in the tax-
onomy. Figure 2 presents the way machines can connect
to the cognitive CPS and exchange information through
cyber network [55] and provide optimised production and
inventory management [56] and lean production [57].
The categorisation in Table 3 is derived from applying
grounded theory to categorise concepts from the exist-
ing literature. The principles of grounded theory demand
that all prominent themes need to be categorised, hence
the emergence of a ‘cyber’ category. However, from our
perspective on cyber security engineering, the cascading
framework contains one error, which is also present in the
literature reviewed.The error is that referring to the middle
layer as‘cyber’demonstrates a different understanding to
that we find in cyber security engineering. Current devel-
opments in industrial systems refer to cyber elements
that are now extending from sensor/actuator through to
supervisory control and advanced analytic solutions. The
grounded theory principles state that we need to report
what we observe, not what we think it is correct or incor-
rect and since cyber is a buzz word, it can refer to many
things. The literature should probably be reworded, but
the taxonomy is based on grounded theory and the fun-
damental principles of grounded theory are applied to
categorise themes from the existing literature. This error
in effect exposes a significant weakness in the current jux-
taposition in the literature of many related systems and
technologies.
Nevertheless, regardless of our disagreement with
the naming one category in Fig. 2, the described cascad-
ing architecture represents a cognitive architecture. The
cognitive architecture allows for learning algorithms and
technologies to be changed quickly and reused on differ-
ent platforms [58] which is necessary in usual CPS situa-
tions, such as when creating multi-vendor and modular
Table 3 The applications
and technologies related to
artificial intelligence for CPS
Connection SAAS BDP, mCPS CBM Self-maintain
Conversion LCM HMI, MaC PHM Self-aware
AMAT LTTA, SDC
Cyber (analytic solutions) EaPS RTD, FoM, AA, PtPM CPS Self-compare
Cognition SCRM POD, SOPS DSS Self-predict
ISaDS ACD, MLA, HPC, ISR Self-optimise
Configuration TaT CoA RCS Self-organise
FPR KPI
AMaAC CPPS Self-configure
Fig. 2 Cascading framework for artificial intelligence for CPS
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production systems. Such reuse can be achieved through
VEO and VEP in CPS, which enable the real-time synchro-
nised coexistence of the virtual and physical dimensions
(see [59]). The emergence of a flaw in the juxtaposition
in the literature in the process of categorising elements
of the CPS cognition confirms that CPS design requires
multi-discipline testing and verification, including system
design and system engineering (see [60]), and requires an
understanding of system sociology [61]. The proposed
cascading architecture operates in a similar method with
social networks, in the sense that individuals can influence
the production line.
The future developments for artificial intelligence
for CPS as presented in Fig. 2, include instruments and
processes to enable energy-aware buildings and cities
(EABaC); physical critical infrastructure with preventive
maintenance (CIPM); and self-correcting cyber-physical
systems (SCCPS) themselves. In addition, the electric
power grid represents one of the largest complex inter-
connected networks [62]. Under stressed conditions, a sin-
gle failure can trigger complex cascading effect, creating
wide-spread failure and blackouts. Flexible AC transmis-
sion systems would enable protection against such cas-
cading failures and distributed energy resource technolo-
gies [63] such as wind power, create additional stress and
vulnerabilities.
4.1 Discussion
The cascading framework in Fig. 2 presents a new way
to design dynamic and automated predictive systems
supported with real-time intelligence.This framework sup-
ports an assessment of the potential for adapting AI cogni-
tive engines in data collection and analytics with dynamic
real-time feedback. These engines might provide predic-
tive intelligence on threat event frequency and the poten-
tial magnitude of resulting losses. Undoubtedly, to pro-
vide this functionality, deep learning algorithms need to
be adopted into cognitive engines to form dynamic con-
fidence intervals and time bound ranges with real-time
data. Once we have these abilities the cascading frame-
work in Fig. 2 becomes a modern tool for risk analytics.
To test whether our proposed framework is more
effective or academically valuable than the traditional
classification method, we used the case study method in
combination with the grounded theory. This study was
funded by Cisco Systems, and we conducted three scop-
ing and verification workshops together, at which we
presented our proposed framework, in comparison with
the existing framework on CPSs [37]. At these workshops,
our proposed framework was judged to be more effec-
tive or academically valuable than the traditional clas-
sification method, in that it includes concepts that have
emerged since the existing framework on CPSs [37] was
established in 2015. Our proposed framework is, in other
words, an updated version that includes new technologi-
cal concepts that have emerged since the establishment
of the existing framework in 2015 [37].
5 Conclusion
The integration of AI into cyber physical systems
has resulted in the rapid emergence of research, and a
juxtaposition in the literature reshaping not only cyber
risk analytics, but also data analytics. This paper reports a
new framework explaining how AI can be integrated
with cyber risk analytics. This confirms that CPS design
requires an understanding of system design, system
engineering and system sociology.
The main findings from this paper include:
1. AI integration in communications networks and con-
nected technology must evolve in an ethical manner
that humans can understand, while maintaining maxi-
mum trust and privacy of the users;
2. The co-ordination of AI in CPS’s must be reliable to
prevent abuse from insider threats, organised crime,
terror organisations or state-sponsored aggressors;
3. Data risk is encouraging the private sector to take
steps to improve the management of confidential and
proprietary information intellectual property and to
protect personally identifiable information;
4. Analysis of a dynamic and self-adopting AI design for a
cognition engine mechanism for the control, analysis,
distribution and management of probabilistic data.
In addition to these findings, this paper applied the
grounded theory to group the requirements for AI in
CPS risk analytics for ‘servitization’ in manufacturing.
The grounded theory analysis was then built into a con-
ceptual diagram, representing a cascading hierarchy
of processes.
Secondly, this paper analysed the requirements for AI in
CPS‘servitization’in manufacturing and presented these in
a taxonomy that supports a robust integration of cyber
risk analytics. The taxonomy details the requirements, as
identified in the literature, for predictive cyber risk analyt-
ics, in which AI components and connectors service the
entire system during its operation. The taxonomy enables
a holistic understanding of the requirements for integrat-
ing cognitive CPS in the cyber risk analytics with dynamic
real-time data from manufacturing and‘servitization’.
7. Vol.:(0123456789)
SN Applied Sciences (2020) 2:1773 | https://doi.org/10.1007/s42452-020-03559-4 Research Article
Acknowledgements Eternal gratitude to the Fulbright Visiting
Scholar Project.
Author contributions Dr. Petar Radanliev: main author; Prof. Dave De
Roure, Prof. MaxVan Kleek: supervision; Dr. Rafael Mantilla Montalvo,
Omar Santos, La’Treall Maddox, Robert Walton, Prof. Pete Burnap,
Eirini Anthi: supervision, review and corrections.
Funding This work was funded by the UK EPSRC [Grant No. EP/
S035362/1] and by the Cisco Research Centre [Grant No. 1525381].
Availability of data and materials All data and materials included in
the article.
Compliance with ethical standard
Conflict of interest On behalf of all authors, the corresponding au-
thor states that there is no conflict or competing interest.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adap-
tation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate
if changes were made.The images or other third party material in this
article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this licence, visit http://creativecommons
.org/licenses/by/4.0/.
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