This document discusses the application of artificial intelligence techniques in cybersecurity. It begins by outlining the motivations for applying AI to cybersecurity, such as the growing amounts of data and connections between devices. It then provides an overview of cybersecurity challenges and common AI techniques like machine learning. The document concludes by presenting several examples of how AI is already being used to enhance cybersecurity, such as through anomaly detection, natural language processing for unstructured threat analysis, and AI-powered security analytics tools.
DATA SCIENCE METHODOLOGY FOR CYBERSECURITY PROJECTS cscpconf
Cybersecurity solutions are traditionally static and signature-based. The traditional solutions
along with the use of analytic models, machine learning and big data could be improved by
automatically trigger mitigation or provide relevant awareness to control or limit consequences
of threats. This kind of intelligent solutions is covered in the context of Data Science for
Cybersecurity. Data Science provides a significant role in cybersecurity by utilising the power
of data (and big data), high-performance computing and data mining (and machine learning) to
protect users against cybercrimes. For this purpose, a successful data science project requires
an effective methodology to cover all issues and provide adequate resources. In this paper, we
are introducing popular data science methodologies and will compare them in accordance with
cybersecurity challenges. A comparison discussion has also delivered to explain methodologies’
strengths and weaknesses in case of cybersecurity projects.
Big data for cybersecurity - skilledfield slides - 25032021Mouaz Alnouri
Now more than ever, the landscape of cybersecurity is getting broader. Both small and large organizations are adopting Big Data technologies to enhance their security detection capabilities.
These slides are from a webinar conducted by Skilledfield, you will learn:
- Why Cybersecurity is a Big Data use case
- How we address Cybersecurity as Big Data Professionals
- How we keep up with the emerging cyber threats
- Benefits of Big Data Technologies for Cybersecurity
Streaming Cyber Security into Graph: Accelerating Data into DataStax Graph an...Keith Kraus
Traditional security tools like security information and event managers (SIEMs) are struggling to keep up with the terabytes of event data (250M to 2B events) being generated each day from an ever-growing number of devices. Cybersecurity has become a data problem, and enterprises need to reply with scalable solutions to enable effective hunting and combat evolving attacks. Rethinking the cybersecurity problem as a data-centric problem led Accenture Labs’s Cybersecurity team to use emerging big data tools along with new approaches such as graph databases and analysis to exploit the connected nature of the data to its advantage. Joshua Patterson, Michael Wendt, and Keith Kraus explain how Accenture Labs’s Cybersecurity team is using Apache Kafka, Spark, and Flink to stream data into Blazegraph and Datastax Graph to accelerate cyber defense.
Leveraging Datastax Graph and Blazegraph allows Accenture Labs to greatly accelerate query and analysis performance compared to traditional security tools like SIEM. Josh, Michael, and Keith share the challenges of fitting cybersecurity data into each of the graph structures, as well as the ways they exploited the connectedness of events to discover new threats that would have been missed in traditional SIEM tools. In addition, they explain how they use GPUs to accelerate graph analysis by using Blazegraph DASL. Josh, Michael, and Keith end by demonstrating how to efficiently and effectively stream data into these graph databases using best-in-breed technologies such as Apache Kafka, Spark, and Flink and touch on why Kudu is becoming an integral part of Accenture’s technology stack. Utilizing these technologies, clients have supercharged their security analysts’ cyber-hunting abilities and are uncovering threats faster.
This report addresses the common challenge of BMS cyber security and its underlying components. Vulnerable elements across a range of components were investigated, with the vulnerabilities potentially affecting more than 10 million people.
During the research, some of the risks discovered within these BMS components include the potential ability for threat actors to:
Remotely lock or unlock doors and gates;
Control physical access of restricted areas;
Deny service (shutdown controllers);
Manipulate alarms and video surveillance;
Control temperature, boilers, air-condition, windows blinds, gas readings, etc.
Through a detailed analysis of the affected components, we provide clear cyber security recommendations for end users, vendors and system integrators, as well as a thorough technical breakdown including Proof of Concept exploit code, which allow unauthenticated remote code execution against the affected BMS products.
https://applied-risk.com/resources/i-own-your-building-management-system
44CON 2014 - Security Analytics Beyond Cyber, Phil Huggins44CON
44CON 2014 - Security Analytics Beyond Cyber, Phil Huggins
A quick summary of the current state of big data technology and data science approaches used in cyber / network defender security analytics including summary use cases, a walk through of a reference architecture and breakdown of the required skills. Focus is on the knowledge needed to run a proof of concept and establish a programme for early benefits. Will then also include a view on the future of extending the platforms and capabilities of security analytics to cover performance metrics and data-driven security management approaches.
This presentation goes through a higher level overview of understanding cyber resilience, important concepts, the difference between cybersecurity and cyber resilience, and frameworks aimed at achieving or assessing an organizations cyber resilience.
DATA SCIENCE METHODOLOGY FOR CYBERSECURITY PROJECTS cscpconf
Cybersecurity solutions are traditionally static and signature-based. The traditional solutions
along with the use of analytic models, machine learning and big data could be improved by
automatically trigger mitigation or provide relevant awareness to control or limit consequences
of threats. This kind of intelligent solutions is covered in the context of Data Science for
Cybersecurity. Data Science provides a significant role in cybersecurity by utilising the power
of data (and big data), high-performance computing and data mining (and machine learning) to
protect users against cybercrimes. For this purpose, a successful data science project requires
an effective methodology to cover all issues and provide adequate resources. In this paper, we
are introducing popular data science methodologies and will compare them in accordance with
cybersecurity challenges. A comparison discussion has also delivered to explain methodologies’
strengths and weaknesses in case of cybersecurity projects.
Big data for cybersecurity - skilledfield slides - 25032021Mouaz Alnouri
Now more than ever, the landscape of cybersecurity is getting broader. Both small and large organizations are adopting Big Data technologies to enhance their security detection capabilities.
These slides are from a webinar conducted by Skilledfield, you will learn:
- Why Cybersecurity is a Big Data use case
- How we address Cybersecurity as Big Data Professionals
- How we keep up with the emerging cyber threats
- Benefits of Big Data Technologies for Cybersecurity
Streaming Cyber Security into Graph: Accelerating Data into DataStax Graph an...Keith Kraus
Traditional security tools like security information and event managers (SIEMs) are struggling to keep up with the terabytes of event data (250M to 2B events) being generated each day from an ever-growing number of devices. Cybersecurity has become a data problem, and enterprises need to reply with scalable solutions to enable effective hunting and combat evolving attacks. Rethinking the cybersecurity problem as a data-centric problem led Accenture Labs’s Cybersecurity team to use emerging big data tools along with new approaches such as graph databases and analysis to exploit the connected nature of the data to its advantage. Joshua Patterson, Michael Wendt, and Keith Kraus explain how Accenture Labs’s Cybersecurity team is using Apache Kafka, Spark, and Flink to stream data into Blazegraph and Datastax Graph to accelerate cyber defense.
Leveraging Datastax Graph and Blazegraph allows Accenture Labs to greatly accelerate query and analysis performance compared to traditional security tools like SIEM. Josh, Michael, and Keith share the challenges of fitting cybersecurity data into each of the graph structures, as well as the ways they exploited the connectedness of events to discover new threats that would have been missed in traditional SIEM tools. In addition, they explain how they use GPUs to accelerate graph analysis by using Blazegraph DASL. Josh, Michael, and Keith end by demonstrating how to efficiently and effectively stream data into these graph databases using best-in-breed technologies such as Apache Kafka, Spark, and Flink and touch on why Kudu is becoming an integral part of Accenture’s technology stack. Utilizing these technologies, clients have supercharged their security analysts’ cyber-hunting abilities and are uncovering threats faster.
This report addresses the common challenge of BMS cyber security and its underlying components. Vulnerable elements across a range of components were investigated, with the vulnerabilities potentially affecting more than 10 million people.
During the research, some of the risks discovered within these BMS components include the potential ability for threat actors to:
Remotely lock or unlock doors and gates;
Control physical access of restricted areas;
Deny service (shutdown controllers);
Manipulate alarms and video surveillance;
Control temperature, boilers, air-condition, windows blinds, gas readings, etc.
Through a detailed analysis of the affected components, we provide clear cyber security recommendations for end users, vendors and system integrators, as well as a thorough technical breakdown including Proof of Concept exploit code, which allow unauthenticated remote code execution against the affected BMS products.
https://applied-risk.com/resources/i-own-your-building-management-system
44CON 2014 - Security Analytics Beyond Cyber, Phil Huggins44CON
44CON 2014 - Security Analytics Beyond Cyber, Phil Huggins
A quick summary of the current state of big data technology and data science approaches used in cyber / network defender security analytics including summary use cases, a walk through of a reference architecture and breakdown of the required skills. Focus is on the knowledge needed to run a proof of concept and establish a programme for early benefits. Will then also include a view on the future of extending the platforms and capabilities of security analytics to cover performance metrics and data-driven security management approaches.
This presentation goes through a higher level overview of understanding cyber resilience, important concepts, the difference between cybersecurity and cyber resilience, and frameworks aimed at achieving or assessing an organizations cyber resilience.
How to perform Secure Data Labeling for Machine LearningSkyl.ai
Data annotations or more commonly called data labeling are an integral part of AI and Machine Learning.
One of the biggest concerns that organizations have while doing AI and ML is handling data.
Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount.
What you will learn:
- Risks associated with data annotations and how to manage data privacy and data protection
- How to handle deployments and infrastructure to manage data security
- How to manage collaborative contributors for secure data labeling to balance scale, security, cost, and quality in data labeling
To Serve and Protect: Making Sense of Hadoop Security Inside Analysis
The Briefing Room with Dr. Robin Bloor and HP Security Voltage
Live Webcast September 22, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=45ece7082b1d7c2cc8179bc7a1a69ea5
Hadoop is rapidly becoming a development platform and dominant server environment, and organizations are keen to take advantage of its massively scalable – and relatively inexpensive – resources. It is not, however, without its limitations, and it often requires a contingent of complementary components in order to behave within an information architecture. One area often overlooked is security, a factor that, if not considered from the onset, can insert great risk when putting sensitive data in Hadoop.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how security was never a design point for Hadoop and what organizations can do about it. He’ll be briefed by Sudeep Venkatesh of HP Security Voltage, who will explain the intricacies surrounding a secure Hadoop implementation. He will show how techniques like format-preserving and partial-field encryption can allow for analytics over protected data, with zero performance impact.
Visit InsideAnalysis.com for more information.
MT74 - Is Your Tech Support Keeping Up with Your Instr TechDell EMC World
Learn the 3 changes Lamar CISD implemented to radically change technical support that enabled teachers to be more productive with technology than ever before! See how Lamar ISD used Dell KACE to improve insight, processes and management to cut total issues by 50% and reduce resolution time by 78%!
5 benefits that ai gives to cloud security venkat k - mediumusmsystem
As cyber threats become more exceptional with each passing year, so should the technologies that businesses achieve to advance cybersecurity and prevent cyberattacks and data exposures.
Time for Your Compliance Check-Up: How Mercy Health Uses Tripwire to Pass AuditsTripwire
Major healthcare providers are tasked with protecting patient data and maintaining complex security compliance requirements enforced through rigorous audits. Mercy Health, a major Midwestern hospital system, became a Tripwire customer in 2013. Using Tripwire technology, they created a successful IT service by integrating their ITSM tool, streamlining their reporting process and more.
Mercy Health and Tripwire show you how to:
-Implement effective change management
-Strengthen security in Epic records systems
-Streamline the audit process
Industry experts share how to embrace the coming merger of information technology (IT) and operation technology (OT) – originally, two very distinct domains of business.
Read more at: http://tripwire.me/adaptitot and www.belden.com/adaptitot
MT81 Keys to Successful Enterprise IoT InitiativesDell EMC World
Success with enterprise Internet of Things (IoT) initiatives begins with strong partnerships between IT and operations technology (OT) organizations and identifying relevant use cases with measurable ROI. Next, choosing the right IoT architecture and technology requires determining the capabilities are needed at the edge and what are needed in the cloud and datacenter to minimize cost and enable analytics-driven action. This session will discusses the challenges involved with introducing sensors and smart devices into your network, including building infrastructure and analytics capabilities , and securing data and applications. Learn how Dell'S IoT-specific gateways, edge analytics software and infrastructure solutions provide flexible architecture options for multiple IoT use cases.
Wolters Kluwer and Risk.Net present the current challenges, priorities and trends influencing banks’ investment in risktech and assesses how they can drive better value in the future. Survey report.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
Cyber attackers are better funded, more focused, and more successful than ever. Making matters worse, defenders have more IT territory to protect, including public cloud, virtual infrastructure, mobile, Internet of Things, and an expanding list of users, applications, and data. An evolution in security strategies is underway; shifting from a preventive approach to one that is more balanced across prevention, monitoring, and response. In this session, we delve into key innovations that enable a more effective defense and how RSA’s NetWitness suite is delivering many of these innovations.
IoT - Data Management Trends, Best Practices, & Use CasesCloudera, Inc.
With billions of new devices, IoT is transforming how businesses capitalize on data. Data driven organizations are using IoT as as a means to improve their customer experience, drive operational efficiencies, and enable new business models. However, without the right data management strategy and tools, investments in IoT can yield limited results.
Join Cloudera and 451 Research for a joint webinar to learn more about some of the data management best practices and how organizations are using advanced analytics and machine learning to enable IoT use cases.
The Quality “Logs”-Jam: Why Alerting for Cybersecurity is Awash with False Po...Mark Underwood
What happens when the (Observe) Plan-Do-Check-Adjust cycle is undermined by lapses in data integrity? Observations are questioned. Plans may be ill-conceived. Actions may be undertaken that undermine rather than enhance. “Checks” can fail. Adjustments may be guesswork. In cybersecurity, the results of poor data integrity can be expensive outages, ransom requests, breaches, fines -- even bankruptcy (think Cambridge Analytica). But data integrity issues take many forms, ranging from benign to malicious. The full range of these issues is surveyed from a cybersecurity perspective, where logs and alerts are critical for defenders -- as well as quality engineers . Techniques borrowed from model-based systems engineering and ontology AI to are identified that can mitigate these deleterious effects on PDCA.
CSPCR: Cloud Security, Privacy and Compliance Readiness - A Trustworthy Fram...IJECEIAES
The privacy, handling, management and security of information in a cloud environment are complex and tedious tasks to achieve. With minimum investment and reduced cost of operations an organization can avail and apply the benefits of cloud computing into its business. This computing paradigm is based upon a pay as per your usage model. Moreover, security, privacy, compliance, risk management and service level agreement are critical issues in cloud computing environment. In fact, there is dire need of a model which can tackle and handle all the security and privacy issues. Therefore, we suggest a CSPCR model for evaluating the preparation of an organization to handle or to counter the threats, hazards in cloud computing environment. CSPCR discusses rules and regulations which are considered as pre-requisites in migrating or shifting to cloud computing services.
Microservices are an effective approach to orchestrate services in the cloud. The microservices architectural style is an approach to develop a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms ( API ).
To be more effective they need a contextual evaluation of the meaning of data of IoT generating always more data.Machine Learning can support Microservices to extract meaning from Big Data making Microservices smarter and speedier. Industries can have huge benefits from this approach.
Cyber risk isn't new, but the stakes grow higher every day. An incident is no longer likely to be an isolated event, but a sustained and persistent campaign. There is no single solution that will offer protection from an attack, but a Cyber Resilience strategy can provide a multi-layered approach that encompasses people, processes and technology. Pete's presentation talks about eliminating the gap between IT and the business to present a united front against threats. This is a paradigm shift that uses security intelligence to guide decisions and support agility.
Combating Cyber Security Using Artificial IntelligenceInderjeet Singh
Cyber Security & Data Protection India Summit 2018 aims to convene the best minds in Cybersecurity under one roof to create an interactive milieu for exchange of knowledge and ideas. The event will endeavour to address the emerging and continuing threats to Cybersecurity and its changing landscape, as well as respond to increasing risk of security breaches and security governance, application security, cloud based security, Network, Mobile and endpoint security and other cyber risks in the India and abroad.
How to perform Secure Data Labeling for Machine LearningSkyl.ai
Data annotations or more commonly called data labeling are an integral part of AI and Machine Learning.
One of the biggest concerns that organizations have while doing AI and ML is handling data.
Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount.
What you will learn:
- Risks associated with data annotations and how to manage data privacy and data protection
- How to handle deployments and infrastructure to manage data security
- How to manage collaborative contributors for secure data labeling to balance scale, security, cost, and quality in data labeling
To Serve and Protect: Making Sense of Hadoop Security Inside Analysis
The Briefing Room with Dr. Robin Bloor and HP Security Voltage
Live Webcast September 22, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=45ece7082b1d7c2cc8179bc7a1a69ea5
Hadoop is rapidly becoming a development platform and dominant server environment, and organizations are keen to take advantage of its massively scalable – and relatively inexpensive – resources. It is not, however, without its limitations, and it often requires a contingent of complementary components in order to behave within an information architecture. One area often overlooked is security, a factor that, if not considered from the onset, can insert great risk when putting sensitive data in Hadoop.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how security was never a design point for Hadoop and what organizations can do about it. He’ll be briefed by Sudeep Venkatesh of HP Security Voltage, who will explain the intricacies surrounding a secure Hadoop implementation. He will show how techniques like format-preserving and partial-field encryption can allow for analytics over protected data, with zero performance impact.
Visit InsideAnalysis.com for more information.
MT74 - Is Your Tech Support Keeping Up with Your Instr TechDell EMC World
Learn the 3 changes Lamar CISD implemented to radically change technical support that enabled teachers to be more productive with technology than ever before! See how Lamar ISD used Dell KACE to improve insight, processes and management to cut total issues by 50% and reduce resolution time by 78%!
5 benefits that ai gives to cloud security venkat k - mediumusmsystem
As cyber threats become more exceptional with each passing year, so should the technologies that businesses achieve to advance cybersecurity and prevent cyberattacks and data exposures.
Time for Your Compliance Check-Up: How Mercy Health Uses Tripwire to Pass AuditsTripwire
Major healthcare providers are tasked with protecting patient data and maintaining complex security compliance requirements enforced through rigorous audits. Mercy Health, a major Midwestern hospital system, became a Tripwire customer in 2013. Using Tripwire technology, they created a successful IT service by integrating their ITSM tool, streamlining their reporting process and more.
Mercy Health and Tripwire show you how to:
-Implement effective change management
-Strengthen security in Epic records systems
-Streamline the audit process
Industry experts share how to embrace the coming merger of information technology (IT) and operation technology (OT) – originally, two very distinct domains of business.
Read more at: http://tripwire.me/adaptitot and www.belden.com/adaptitot
MT81 Keys to Successful Enterprise IoT InitiativesDell EMC World
Success with enterprise Internet of Things (IoT) initiatives begins with strong partnerships between IT and operations technology (OT) organizations and identifying relevant use cases with measurable ROI. Next, choosing the right IoT architecture and technology requires determining the capabilities are needed at the edge and what are needed in the cloud and datacenter to minimize cost and enable analytics-driven action. This session will discusses the challenges involved with introducing sensors and smart devices into your network, including building infrastructure and analytics capabilities , and securing data and applications. Learn how Dell'S IoT-specific gateways, edge analytics software and infrastructure solutions provide flexible architecture options for multiple IoT use cases.
Wolters Kluwer and Risk.Net present the current challenges, priorities and trends influencing banks’ investment in risktech and assesses how they can drive better value in the future. Survey report.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
Cyber attackers are better funded, more focused, and more successful than ever. Making matters worse, defenders have more IT territory to protect, including public cloud, virtual infrastructure, mobile, Internet of Things, and an expanding list of users, applications, and data. An evolution in security strategies is underway; shifting from a preventive approach to one that is more balanced across prevention, monitoring, and response. In this session, we delve into key innovations that enable a more effective defense and how RSA’s NetWitness suite is delivering many of these innovations.
IoT - Data Management Trends, Best Practices, & Use CasesCloudera, Inc.
With billions of new devices, IoT is transforming how businesses capitalize on data. Data driven organizations are using IoT as as a means to improve their customer experience, drive operational efficiencies, and enable new business models. However, without the right data management strategy and tools, investments in IoT can yield limited results.
Join Cloudera and 451 Research for a joint webinar to learn more about some of the data management best practices and how organizations are using advanced analytics and machine learning to enable IoT use cases.
The Quality “Logs”-Jam: Why Alerting for Cybersecurity is Awash with False Po...Mark Underwood
What happens when the (Observe) Plan-Do-Check-Adjust cycle is undermined by lapses in data integrity? Observations are questioned. Plans may be ill-conceived. Actions may be undertaken that undermine rather than enhance. “Checks” can fail. Adjustments may be guesswork. In cybersecurity, the results of poor data integrity can be expensive outages, ransom requests, breaches, fines -- even bankruptcy (think Cambridge Analytica). But data integrity issues take many forms, ranging from benign to malicious. The full range of these issues is surveyed from a cybersecurity perspective, where logs and alerts are critical for defenders -- as well as quality engineers . Techniques borrowed from model-based systems engineering and ontology AI to are identified that can mitigate these deleterious effects on PDCA.
CSPCR: Cloud Security, Privacy and Compliance Readiness - A Trustworthy Fram...IJECEIAES
The privacy, handling, management and security of information in a cloud environment are complex and tedious tasks to achieve. With minimum investment and reduced cost of operations an organization can avail and apply the benefits of cloud computing into its business. This computing paradigm is based upon a pay as per your usage model. Moreover, security, privacy, compliance, risk management and service level agreement are critical issues in cloud computing environment. In fact, there is dire need of a model which can tackle and handle all the security and privacy issues. Therefore, we suggest a CSPCR model for evaluating the preparation of an organization to handle or to counter the threats, hazards in cloud computing environment. CSPCR discusses rules and regulations which are considered as pre-requisites in migrating or shifting to cloud computing services.
Microservices are an effective approach to orchestrate services in the cloud. The microservices architectural style is an approach to develop a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms ( API ).
To be more effective they need a contextual evaluation of the meaning of data of IoT generating always more data.Machine Learning can support Microservices to extract meaning from Big Data making Microservices smarter and speedier. Industries can have huge benefits from this approach.
Cyber risk isn't new, but the stakes grow higher every day. An incident is no longer likely to be an isolated event, but a sustained and persistent campaign. There is no single solution that will offer protection from an attack, but a Cyber Resilience strategy can provide a multi-layered approach that encompasses people, processes and technology. Pete's presentation talks about eliminating the gap between IT and the business to present a united front against threats. This is a paradigm shift that uses security intelligence to guide decisions and support agility.
Combating Cyber Security Using Artificial IntelligenceInderjeet Singh
Cyber Security & Data Protection India Summit 2018 aims to convene the best minds in Cybersecurity under one roof to create an interactive milieu for exchange of knowledge and ideas. The event will endeavour to address the emerging and continuing threats to Cybersecurity and its changing landscape, as well as respond to increasing risk of security breaches and security governance, application security, cloud based security, Network, Mobile and endpoint security and other cyber risks in the India and abroad.
AI Cybersecurity: Pros & Cons. AI is reshaping cybersecurityTasnim Alasali
Discover how AI is reshaping cybersecurity. This presentation delves into AI's role in enhancing threat detection, the balance of innovation and risk, and the strategies shaping the future of digital defense.
International Journal on Cryptography and Information Security (IJCIS)ijcisjournal
International Journal on Cryptography and Information Security ( IJCIS) is an open access peer reviewed journal that focuses on cutting-edge results in applied cryptography and Information security. It aims to bring together scientists, researchers and students to exchange novel ideas and results in all aspects of cryptography, coding and Information security.
Security in the age of Artificial IntelligenceFaction XYZ
Keynote Presentation for ISACA Belgium 2017 on how artificial intelligence is influencing the cyber security industry, and what current and future developments there are
DSS and Security Intelligence @IBM_Connect_2014_AprilAndris Soroka
DSS participated in this year's "IBM Connect" event organized by regional IBM's VAD - ALSO Baltics. DSS spoke about importance of IT Security in new - digital world that is developing. New technologies bring new business opportunities but as well bring also new security threats and risks that have to be considered in first place.
Next Generation Defense in Depth Model - Tari Schreider, CCISO, Chief Cybers...EC-Council
This session will focus on presenting a next generation defense in depth model and answer the question on many CISO’s minds - is it still relevant? A model of defense in depth will serve as a backdrop to introduce you to a wide range of solutions from across the cybersecurity-industrial complex that just may change how you view your defense in depth approach.
Cyber Risk Management in 2017 - Challenges & RecommendationsUlf Mattsson
With cyber attacks on the rise, securing your data is more imperative than ever. In future, organizations will face severe penalties if their data isn’t robustly secured. This will have a far reaching impact for how businesses deal with security in terms of managing their cyber risk.
Join this presentation to learn the cyber security controls prescribed by regulation, how this impacts compliance, and how cyber risk management helps CISOs understand the degree these controls are in place and where to prioritize their cyber dollars and ensure they are not at risk for fines.
Viewers will learn:
- The latest cybercrime trends and targets
- Trends in board involvement in cybersecurity
- How to effectively manage the full range of enterprise risks
- How to protect against ransomware
- Visibility into third party risk
- Data security metrics
Cognitive automation with machine learning in cyber securityRishi Kant
These slides deck is from # HITBGSEC Singapore 2018, It consists of integration of cognitive automation and machine learning in different cyber security processes
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
The world we live in right now is getting more and more digital. All possible things we were reading in sci-fi books or watching in fantasy movies are becoming a reality. Internet of things, drones, e-world, mobility, applications, cloud, digital prototyping, e-voting, quantum computing, 3D printing like in Terminator movies and much more is a reality. On average auditory of this room can agree that it is ok to say that we live in the future. As what has happened to technology for personal use and business in last 25 years is impressive. And we can experience that. We are unique generation and live in unique times.
The digital world gives huge opportunities to any business entering it. There are soon close to 4 billion of potential customers out there in 2015 that are. Digital world introduces new products every day and technology creators are extremely working on to get new products to market as soon as possible.
But like in every book, movie, story, historical reality when there are good forces also there are bad forces. Cyber crime is growing and various things are happening everywhere. New technologies also introduce new risks and those risks are with different configuration. Countries attack countries and we call that a cyber wars, citizens are attacking countries and we call that hacktivism, professionals are attacking everyone for financial gains and we call that organized digital crime. And the methods are getting more and more sophisticated so in the end doesn’t matter how great are technologies of defense every day we have new articles of new indicents, data breeches, companies who have huge financial loses and damages of reputation, lost marketplace, stock market positions, customers, employees or even lives. I won’t touch each different method of attacks but I will simply try to share how we as a system integrator of complex cyber security protection technology solutions look at things and protect our customers.
A review: Artificial intelligence and expert systems for cyber securitybijejournal
Artificial intelligence (AI) and expert systems are essential and vital tools to counter potentially dangerous threats
in cyber security. The protection of data requires skilled cyber security technicians for various types of roles. The
essential role of an expert system is to monitor the threats and assist the technician to strengthen security. The
system uses various datasets like a machine and deep learning as well as reinforced learning in order to make
intelligent decisions. The Internet of Things (IoT) is one of the major concerns for cyber security because it is
potentially the second most likely vulnerable link in the cyber security environment because an attacker can easily
gain access to the system by breaching any IoT device that is connected to the system. Still human is the strongest
and potentially the weakest link in the cyber security environment. This review intends to present AI and expert
systems for cyber security
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
27. AI Booming
1. Computing power
What makes AI credible this time around . . .
2. ML, deep learning
algorithms
3. Big data
4. Age of the customer/
digital demand
5. Huge investments
Source: “Artificial Intelligence Can Finally Unleash Your Business Applications' Creativity” Forrester report
28. AI is in Charge
Stock Market: 75+% of all trade orders
generated by Automated Trading Systems
Aviation: Uninterruptible Autopilot System
Military: Nuclear
Weapons
Energy: Nuclear
Power Plants
Utilities: Water
Plants/Electrical Grid
Communications: Satellites
29. AI Applications
Health
Personalized medicine, image analytics
Manufacturing
Predictive and prescriptive maintenance
Consumer tech
Chatbots
Financial services
Fraud detection, ID verification
Government
Cyber-security, smart cities and utilities
Energy
Seismic and reservoir modeling
Service providers
Media delivery
Retail
Video surveillance, shopping patterns
34. AI Ecosystem
IT
Data
Data Platforms
Data Science and ML
/ DL Tools
Solutions
Genome Research Video SurveillanceCustomer 360
Example Industry Use Cases
Fraud Detection
HDFS/NFS
User Access Security Time to Deploy Multi-Tenant
Data DuplicationData Store Cloud
Infrastructure
55. Artificial Neural Networks
Chen, Y., Abraham, A., & Yang, B. (2007). Hybrid flexible neural-tree-based
intrusion detection systems. International Journal of Intelligent Systems, 22,
337–352.
56. Stein, G., Chen, B., Wu, A. S., & Hua, K. A. (2005). Decision tree classifier for network intrusion detection with GA-based feature selection. In
Paper presented at the proceedings of the 43rd annual Southeast regional conference. Kennesaw, Georgia.
Randomly
Generated
Population
Feature
Selection
Decision Tree
Constructor
Decision Tree
Evaluator
Fitness
Computation
Final Decision
Tree
Classifier
Training Data
Validation
Data
Testing
Data
Generate Next Generation
GA/Decision Tree Hybrid
Genetic Algorithms
57. Teache
r
Correct
(No Training)
Winner
(Decision)
w1 w2 w3 wn
Φ1 Φ2 Φ3 Φn
Y(1) Y(2) Y(3) Y(n)
X(1) X(2) X(3) X(4)
Incorrect
(Training Needed)
Chavan, Sampada, et al. "Adaptive neuro-fuzzy intrusion
detection systems. "Information Technology: Coding and
Computing, 2004. Proceedings. ITCC 2004. International
Conference on. Vol. 1. IEEE, 2004.
Neuro-fuzzing
59. Shon, T., & Moon, J. (2007). A hybrid machine learning approach to network anomaly detection. Information Sciences, 177, 3799–3821.
Hybrid ML NAD
60. Multiple Classifier System for Intrusion Detection
Intrusion Detection as a Pattern Recognition Problem
Giacinto, Giorgio, Fabio Roli, and Luca Didaci. "Fusion of multiple classifiers for intrusion detection in computer networks." Pattern recognition letters 24.12
(2003): 1795-1803.
Pattern Recognition
61. Neural Networks
(Backpropagation)
Neural Networks (Scale
Conjugate Gradient)
Neural Network (One Step
Secant)
Support Vector Machine
Multivariate Regression
Splines
Ensemble
Data
preprocessor
Mukkamala, Srinivas, Andrew H. Sung, and Ajith Abraham. "Intrusion detection using an ensemble of intelligent
paradigms." Journal of network and computer applications 28.2 (2005): 167-182.
Ensemble