The document summarizes research on intrusion detection and prevention systems. It discusses how intrusion detection systems originally detected attacks but did not prevent them, while intrusion prevention systems aim to detect and block attacks early through techniques like stateful pattern matching, protocol analysis, and heuristics. The document also examines challenges for intrusion prevention systems, such as reducing false alarms, handling large data volumes, developing unified integration solutions, and sensor management.
The Challenges, Gaps and Future Trends: Network SecurityDeris Stiawan
This document discusses several challenges and future trends in network security, including network attacks, forensic investigation, cloud computing, heterogeneous networks, network graphs, network management, big data processing, and the Internet of Things. It provides examples of existing research and identifies opportunities for new research in tools and methods for defense, data analysis, clustering/classification, security mechanisms, privacy, quality of service, monitoring, and data processing in these domains.
Ja'far Alqatawna presented on applying data mining in security. He discussed trends showing that while encryption and defenses have advanced, security incidents are increasing. Factors contributing to insecurity include new technologies, connectivity, extensibility, and complexity. Data mining can help by analyzing large security data through classification, clustering, prediction, and correlation. Alqatawna discussed applying data mining for behavioral biometrics authentication, malicious spam detection, cybercrime detection, and adaptive security. He outlined two ongoing research projects on malicious spam detection in educational email systems and on social networks.
A Data Hiding Techniques Based on Length of English Text using DES and Attack...IJORCS
The comparing recent proposal for multimedia applications network security remains an important topic for researchers. The security deals with both wired and wireless communication. Network is defined as it is a large system consisting of many similar parts that are connected together to allow the movement or communication between or along the parts or between the parts and a control center. There are the main components of the network information system such as end systems (terminals, servers) and intermediate systems (hubs, switches, gateways). Every node has its own set of vulnerabilities that can be related to hardware, software, protocol stack etc. Nodes are interconnected by physical supports in a network for example connected with cables in wired Local Area Network (LAN) or radio waves (Wi-Fi) in Wireless Local Area Network (WLAN). Some nodes are able to provide services (FTP, HTTP browsing, database access). If two nodes want to communicate together, they must be interconnected physically and logically. Network security deals with also information hiding technique. Now day’s security deals with heterogeneous networks. The use of different wireless and wired network which are working on different platform is heterogeneous. So design of network security for such type of heterogeneous network is difficult task.
Research on security of the wlan campus networkkarthik
The document summarizes research on securing the wireless local area network (WLAN) campus network. It outlines the benefits of WLANs, existing security threats and controls, and proposes a new security control program that includes access control, content filtering, data encryption, user management, and log auditing to provide a more secure digital campus network. WLANs are expected to play an increasingly important role on college campuses due to their high transmission speeds and flexibility.
Security against Web Application Attacks Using Ontology Based Intrusion Detec...IRJET Journal
The document presents a proposed ontology-based intrusion detection system to provide security against web application attacks. The system aims to address limitations of existing signature-based intrusion detection systems, such as high false positive and negative rates. The proposed system uses an ontology created with Protege to model web application attacks, vulnerabilities, threats and security controls. Rules are also defined to allow the system to predict and classify attacks based on the ontology. The system architecture includes components for system analysis, interface, rule engine, ontology generation and a knowledge base. The system is evaluated on its ability to detect common web attacks like SQL injection, cross-site scripting and buffer overflow attacks.
Malicious activities (malcodes) are self replicating
malware and a major security threat in a network environment.
Timely detection and system alert flags are very essential to
prevent rapid malcodes spreading in the network. The difficulty
in detecting malcodes is that they evolve over time. Despite the fact
that signature-based tools, are generally used to secure systems,
signature-based malcode detectors neglect to recognize muddled
and beforehand concealed malcode executables. Automatic signature
generation systems has likewise been use to address the issue
of malcodes, yet there are many works required for good detection.
Base on the behavior way of malcodes, a behavior approach is
required for such detection. Specifically, we require a dynamic
investigation and behavior Rule Base system that distinguishes
malcodes without erroneously block legitimate traffic or increase
false alarms. This paper proposed and discussed the approach
using Machine learning and Indicators of Compromise (IOC) to
analyze intrusion in a network, to identify the cause of the attack
and to provide future detection. This paper proposed the use of
behaviour malware analysis framework to analyze intrusion data,
apply clustering algorithm on the analyzed data and generate IOC
from the clustered data for IOCRule, which will be implemented
into Snort Intrusion Detection System (IDS) for malicious code
detection.
The document summarizes research on intrusion detection and prevention systems. It discusses how intrusion detection systems originally detected attacks but did not prevent them, while intrusion prevention systems aim to detect and block attacks early through techniques like stateful pattern matching, protocol analysis, and heuristics. The document also examines challenges for intrusion prevention systems, such as reducing false alarms, handling large data volumes, developing unified integration solutions, and sensor management.
The Challenges, Gaps and Future Trends: Network SecurityDeris Stiawan
This document discusses several challenges and future trends in network security, including network attacks, forensic investigation, cloud computing, heterogeneous networks, network graphs, network management, big data processing, and the Internet of Things. It provides examples of existing research and identifies opportunities for new research in tools and methods for defense, data analysis, clustering/classification, security mechanisms, privacy, quality of service, monitoring, and data processing in these domains.
Ja'far Alqatawna presented on applying data mining in security. He discussed trends showing that while encryption and defenses have advanced, security incidents are increasing. Factors contributing to insecurity include new technologies, connectivity, extensibility, and complexity. Data mining can help by analyzing large security data through classification, clustering, prediction, and correlation. Alqatawna discussed applying data mining for behavioral biometrics authentication, malicious spam detection, cybercrime detection, and adaptive security. He outlined two ongoing research projects on malicious spam detection in educational email systems and on social networks.
A Data Hiding Techniques Based on Length of English Text using DES and Attack...IJORCS
The comparing recent proposal for multimedia applications network security remains an important topic for researchers. The security deals with both wired and wireless communication. Network is defined as it is a large system consisting of many similar parts that are connected together to allow the movement or communication between or along the parts or between the parts and a control center. There are the main components of the network information system such as end systems (terminals, servers) and intermediate systems (hubs, switches, gateways). Every node has its own set of vulnerabilities that can be related to hardware, software, protocol stack etc. Nodes are interconnected by physical supports in a network for example connected with cables in wired Local Area Network (LAN) or radio waves (Wi-Fi) in Wireless Local Area Network (WLAN). Some nodes are able to provide services (FTP, HTTP browsing, database access). If two nodes want to communicate together, they must be interconnected physically and logically. Network security deals with also information hiding technique. Now day’s security deals with heterogeneous networks. The use of different wireless and wired network which are working on different platform is heterogeneous. So design of network security for such type of heterogeneous network is difficult task.
Research on security of the wlan campus networkkarthik
The document summarizes research on securing the wireless local area network (WLAN) campus network. It outlines the benefits of WLANs, existing security threats and controls, and proposes a new security control program that includes access control, content filtering, data encryption, user management, and log auditing to provide a more secure digital campus network. WLANs are expected to play an increasingly important role on college campuses due to their high transmission speeds and flexibility.
Security against Web Application Attacks Using Ontology Based Intrusion Detec...IRJET Journal
The document presents a proposed ontology-based intrusion detection system to provide security against web application attacks. The system aims to address limitations of existing signature-based intrusion detection systems, such as high false positive and negative rates. The proposed system uses an ontology created with Protege to model web application attacks, vulnerabilities, threats and security controls. Rules are also defined to allow the system to predict and classify attacks based on the ontology. The system architecture includes components for system analysis, interface, rule engine, ontology generation and a knowledge base. The system is evaluated on its ability to detect common web attacks like SQL injection, cross-site scripting and buffer overflow attacks.
Malicious activities (malcodes) are self replicating
malware and a major security threat in a network environment.
Timely detection and system alert flags are very essential to
prevent rapid malcodes spreading in the network. The difficulty
in detecting malcodes is that they evolve over time. Despite the fact
that signature-based tools, are generally used to secure systems,
signature-based malcode detectors neglect to recognize muddled
and beforehand concealed malcode executables. Automatic signature
generation systems has likewise been use to address the issue
of malcodes, yet there are many works required for good detection.
Base on the behavior way of malcodes, a behavior approach is
required for such detection. Specifically, we require a dynamic
investigation and behavior Rule Base system that distinguishes
malcodes without erroneously block legitimate traffic or increase
false alarms. This paper proposed and discussed the approach
using Machine learning and Indicators of Compromise (IOC) to
analyze intrusion in a network, to identify the cause of the attack
and to provide future detection. This paper proposed the use of
behaviour malware analysis framework to analyze intrusion data,
apply clustering algorithm on the analyzed data and generate IOC
from the clustered data for IOCRule, which will be implemented
into Snort Intrusion Detection System (IDS) for malicious code
detection.
WEARABLE TECHNOLOGY DEVICES SECURITY AND PRIVACY VULNERABILITY ANALYSISIJNSA Journal
Wearable Technology also called wearable gadget, is acategory of technology devices with low processing
capabilities that can be worn by a user with the aim to provide information and ease of access to the master
devices its pairing with. Such examples are Google Glass and Smart watch. The impact of wearable
technology becomes significant when people start their invention in wearable computing, where their
mobile devices become one of the computation sources. However, wearable technology is not mature yet in
term of device security and privacy acceptance of the public. There exists some security weakness that
prompts such wearable devices vulnerable to attack. One of the critical attack on wearable technology is
authentication issue. The low processing due to less computing power of wearable device causethe
developer's inability to equip some complicated security mechanisms and algorithm on the device.In this
study, an overview of security and privacy vulnerabilities on wearable devices is presented.
DESIGN AND IMPLEMENTATION OF THE ADVANCED CLOUD PRIVACY THREAT MODELING IJNSA Journal
Privacy-preservation for sensitive data has become a challenging issue in cloud computing. Threat
modeling as a part of requirements engineering in secure software development provides a structured
approach for identifying attacks and proposing countermeasures against the exploitation of vulnerabilities
in a system. This paper describes an extension of Cloud Privacy Threat Modeling (CPTM) methodology for
privacy threat modeling in relation to processing sensitive data in cloud computing environments. It
describes the modeling methodology that involved applying Method Engineering to specify characteristics
of a cloud privacy threat modeling methodology, different steps in the proposed methodology and
corresponding products. In addition, a case study has been implemented as a proof of concept to
demonstrate the usability of the proposed methodology. We believe that the extended methodology
facilitates the application of a privacy-preserving cloud software development approach from requirements
engineering to design.
This document provides an introduction to steganography. It defines steganography as concealing a file within another file by hiding information in images, audio, or video. The document outlines the history of steganography and its applications. It also discusses basic terminology, fields related to information hiding, steganalysis, and some common steganography tools. The document concludes with describing steganographic techniques such as least significant bit substitution and exercises for readers.
Network infrastructures have played important part in most daily communications for business industries,
social networking, government sectors and etc. Despites the advantages that came from such
functionalities, security threats have become a daily struggle. One major security threat is hacking.
Consequently, security experts and researchers have suggested possible security solutions such as
Firewalls, Intrusion Detection Systems (IDS), Intrusion Detection and Prevention Systems (IDP) and
Honeynet. Yet, none of these solutions have proven their ability to completely address hacking. The reason
behind that, there is a few researches that examine the behavior of hackers. This paper formally and
practically examines in details the behavior of hackers and their targeted environments. Moreover, this
paper formally examines the properties of one essential pre-hacking step called scanning and highlights its
importance in developing hacking strategies. Also, it illustrates the properties of hacking that is common in
most hacking strategies to assist security experts and researchers towards minimizing the risk of hack.
THE INTERNET OF THINGS: NEW INTEROPERABILITY, MANAGEMENT AND SECURITY CHALLENGESIJNSA Journal
The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It
extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates
opportunities in numerous domains. However, this increase in connectivity creates many prominent
challenges. This paper provides a survey of some of the major issues challenging the widespread adoption
of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the
IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security,
management, and privacy.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The document presents a PhD synopsis on network security and quality of service. It discusses common security attacks such as eavesdropping, viruses, worms, Trojans, phishing, IP spoofing, and denial of service. It also covers security technologies including cryptographic systems, firewalls, intrusion detection systems, anti-malware software, and secure socket layer. The proposed research focuses on enhancing data security through improved encryption methods and dynamic bandwidth allocation to improve quality of service for different data types. The goal is to develop more secure and efficient network communication.
Intrusion Detection Techniques In Mobile NetworksIOSR Journals
This document discusses intrusion detection techniques for mobile networks. It begins by outlining the vulnerabilities of wireless networks, including the open medium, dynamic topology, lack of centralized monitoring, and cooperative algorithms. It then explains the need for intrusion detection systems, as completely preventing intrusions is unrealistic. The document classifies intrusion detection systems and outlines their requirements, including continuous monitoring, fault tolerance, and adaptability. It concludes by describing the two main techniques of intrusion detection: anomaly detection, which flags deviations from a normal activity profile; and misuse detection, which searches for patterns matching known attacks.
The document proposes a security model for wireless sensor networks using zero knowledge protocol. It addresses security threats like cloning attacks, man-in-the-middle attacks, and replay attacks. The model uses a unique fingerprint for each node based on its neighboring nodes to detect cloning. It also uses zero knowledge protocol for sensor nodes to verify authenticity without transmitting cryptographic information, preventing man-in-the-middle and replay attacks. The paper analyzes the performance and security of the proposed model.
IRJET- An Intrusion Detection Framework based on Binary Classifiers Optimized...IRJET Journal
This document proposes an intrusion detection framework that uses multiple binary classifiers optimized by a genetic algorithm. It analyzes decision trees, naive Bayes, and support vector machines to classify network connections as normal or attacks based on the NSL-KDD dataset. The classifiers are aggregated and a genetic algorithm is used to generate high-quality solutions. Experimental results show that the proposed method achieves 99% accuracy in intrusion detection, outperforming single classification techniques. The goal is to develop an application that can efficiently process network data and identify intrusion risks.
A technical review and comparative analysis of machine learning techniques fo...IJECEIAES
Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network (Inception-CNN), Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning models in MANET.
This document outlines an agenda for discussing computer security policies and procedures. It covers topics such as introducing security policies, network architecture and security services, handling security incidents, ongoing security activities, and resources for security. The introduction defines key terms and outlines a basic approach to developing a security plan. It emphasizes identifying assets, threats, and implementing cost-effective protection measures.
Wireless Networks Security in Jordan: A Field StudyIJNSA Journal
- The document summarizes a study that evaluated the security of wireless networks in Jordan through a process called "wardriving" where the researchers drove around with wireless network detection tools.
- The results found that the majority (79.52%) of wireless networks tested were unsecured and vulnerable. Most networks used either low levels of encryption (68.67%) or no encryption at all (11.45%).
- Nearly all networks broadcast the default SSID (92.17%), leaving them exposed to potential hackers since changing the SSID is a basic security precaution.
Multi-Tiered Communication Security Schemes in Wireless Ad-Hoc Sensor NetworksIDES Editor
Networks of wireless micro-sensors for monitoring
physical environments have emerged as an important new
application area for wireless technology. Key attributes of
these new types of networked systems are the severely
constrained computational and energy resources and an ad
hoc operational environment. This paper is a study of the
communication security aspects of these networks. Resource
limitations and specific architecture of sensor networks call
for customized security mechanisms. Our approach is to
classify the types of data existing in sensor networks, and
identify possible communication security threats according
to that classification. We propose a communication security
scheme where for each type of data we define a corresponding
security mechanism. By employing this multi-tiered security
architecture where each mechanism has different resource
requirements, we allow for efficient resource management,
which is essential for wireless sensor networks.
This research proposal aims to develop autonomous post-intrusion network isolation systems using neural networks, rule-sets, and mathematical models. The research has four main goals: 1) investigate techniques to prevent comprehensive network infiltration if a system is compromised, 2) investigate proactive user auditing to mitigate fraud risk, 3) provide a model for network forensics after an intrusion, and 4) demonstrate a practical implementation. The methodology will include a literature review, mathematical modeling, analyzing isolation scenarios, and developing software. The research will be conducted over three semesters, with deliverables including a literature review, network isolation process, prototype architecture in the first semester, software development in the second, and testing/refinement in the
This document provides background information on the history and importance of network security. It discusses how the advent of the internet led to security becoming a major concern, as the internet's architecture allowed for many security threats. The document outlines the internet and network security timeline, from the creation of the ARPANET in 1969 to the crimes of Kevin Mitnick in the 1990s that heightened awareness of information security. It also examines the differences between data security and network security, and how a layered security model corresponds to the OSI model layers.
A comprehensive study on classification of passive intrusion and extrusion de...csandit
Cyber criminals compromise Integrity, Availability and Confidentiality of network resources in
cyber space and cause remote class intrusions such as U2R, R2L, DoS and probe/scan system
attacks .To handle these intrusions, Cyber Security uses three audit and monitoring systems
namely Intrusion Prevention Systems (IPS), Intrusion Detection Systems (IDS). Intrusion
Detection System (IDS) monitors only inbound traffic which is insufficient to prevent botnet
systems. A system to monitor outbound traffic is named as Extrusion Detection System (EDS).
Therefore a hybrid system should be designed to handle both inbound and outbound traffic.
Due to the increased false alarms preventive systems do not suite to an organizational network.
The goal of this paper is to devise a taxonomy for cyber security and study the existing methods
of Intrusion and Extrusion Detection systems based on three primary characteristics. The
metrics used to evaluate IDS and EDS are also presented.
Multi-stage secure clusterhead selection using discrete rule-set against unkn...IJECEIAES
The document discusses a proposed multi-stage secure clusterhead selection technique for wireless sensor networks using a discrete rule-set. The technique aims to securely select clusterheads during the data aggregation process and learn the nature of communications to gain knowledge about adversary intensity. It constructs primary and secondary rule-sets to filter and select secure clusterheads based on energy, neighbors, vulnerability, vicinity and distance from adversaries. Simulation results using MEMSIC sensor nodes showed the proposed approach reduces energy consumption and improves data delivery compared to existing methods.
This document discusses using data mining techniques to detect spyware. It begins by defining spyware and artificial intelligence. It then discusses three AI approaches that have been applied to spyware detection: heuristic technology, neural network technology, and data mining techniques. It focuses on using breadth-first search (BFS) within a data mining approach. The document finds that data mining techniques achieve an overall accuracy of 90.5% in detecting spyware, performing better than traditional signature-based or heuristic-based methods.
Using Machine Learning in Networks Intrusion Detection SystemsOmar Shaya
The internet and different computing devices from desktop computers to smartphones have raised many security and privacy concerns, and the need to automate systems that detect attacks on these networks has emerged in order to be able to protect these networks with scale. And while traditional intrusion detection methods may be able to detect previously known attacks, the issue of dealing with new unknown attacks arises and that brings machine learning as a strong candidate to solve these challenges.
In this report, we investigate the use of machine learning in detecting network attacks, intrusion detection, by looking at work that has been done in this field. Particularly we look at the work that has been done by Pasocal et al.
This document provides an overview of the features and capabilities of FortiGate IPS (Intrusion Prevention System), including over 7,000 signatures, DOS protection, deployment options, sensor functions, FortiGuard security services, performance metrics, packet logging, user quarantine, advanced NGIPS features, and DOS sensors. It also includes contact information for an Italian company specialized in Fortinet solutions.
WEARABLE TECHNOLOGY DEVICES SECURITY AND PRIVACY VULNERABILITY ANALYSISIJNSA Journal
Wearable Technology also called wearable gadget, is acategory of technology devices with low processing
capabilities that can be worn by a user with the aim to provide information and ease of access to the master
devices its pairing with. Such examples are Google Glass and Smart watch. The impact of wearable
technology becomes significant when people start their invention in wearable computing, where their
mobile devices become one of the computation sources. However, wearable technology is not mature yet in
term of device security and privacy acceptance of the public. There exists some security weakness that
prompts such wearable devices vulnerable to attack. One of the critical attack on wearable technology is
authentication issue. The low processing due to less computing power of wearable device causethe
developer's inability to equip some complicated security mechanisms and algorithm on the device.In this
study, an overview of security and privacy vulnerabilities on wearable devices is presented.
DESIGN AND IMPLEMENTATION OF THE ADVANCED CLOUD PRIVACY THREAT MODELING IJNSA Journal
Privacy-preservation for sensitive data has become a challenging issue in cloud computing. Threat
modeling as a part of requirements engineering in secure software development provides a structured
approach for identifying attacks and proposing countermeasures against the exploitation of vulnerabilities
in a system. This paper describes an extension of Cloud Privacy Threat Modeling (CPTM) methodology for
privacy threat modeling in relation to processing sensitive data in cloud computing environments. It
describes the modeling methodology that involved applying Method Engineering to specify characteristics
of a cloud privacy threat modeling methodology, different steps in the proposed methodology and
corresponding products. In addition, a case study has been implemented as a proof of concept to
demonstrate the usability of the proposed methodology. We believe that the extended methodology
facilitates the application of a privacy-preserving cloud software development approach from requirements
engineering to design.
This document provides an introduction to steganography. It defines steganography as concealing a file within another file by hiding information in images, audio, or video. The document outlines the history of steganography and its applications. It also discusses basic terminology, fields related to information hiding, steganalysis, and some common steganography tools. The document concludes with describing steganographic techniques such as least significant bit substitution and exercises for readers.
Network infrastructures have played important part in most daily communications for business industries,
social networking, government sectors and etc. Despites the advantages that came from such
functionalities, security threats have become a daily struggle. One major security threat is hacking.
Consequently, security experts and researchers have suggested possible security solutions such as
Firewalls, Intrusion Detection Systems (IDS), Intrusion Detection and Prevention Systems (IDP) and
Honeynet. Yet, none of these solutions have proven their ability to completely address hacking. The reason
behind that, there is a few researches that examine the behavior of hackers. This paper formally and
practically examines in details the behavior of hackers and their targeted environments. Moreover, this
paper formally examines the properties of one essential pre-hacking step called scanning and highlights its
importance in developing hacking strategies. Also, it illustrates the properties of hacking that is common in
most hacking strategies to assist security experts and researchers towards minimizing the risk of hack.
THE INTERNET OF THINGS: NEW INTEROPERABILITY, MANAGEMENT AND SECURITY CHALLENGESIJNSA Journal
The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It
extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates
opportunities in numerous domains. However, this increase in connectivity creates many prominent
challenges. This paper provides a survey of some of the major issues challenging the widespread adoption
of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the
IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security,
management, and privacy.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The document presents a PhD synopsis on network security and quality of service. It discusses common security attacks such as eavesdropping, viruses, worms, Trojans, phishing, IP spoofing, and denial of service. It also covers security technologies including cryptographic systems, firewalls, intrusion detection systems, anti-malware software, and secure socket layer. The proposed research focuses on enhancing data security through improved encryption methods and dynamic bandwidth allocation to improve quality of service for different data types. The goal is to develop more secure and efficient network communication.
Intrusion Detection Techniques In Mobile NetworksIOSR Journals
This document discusses intrusion detection techniques for mobile networks. It begins by outlining the vulnerabilities of wireless networks, including the open medium, dynamic topology, lack of centralized monitoring, and cooperative algorithms. It then explains the need for intrusion detection systems, as completely preventing intrusions is unrealistic. The document classifies intrusion detection systems and outlines their requirements, including continuous monitoring, fault tolerance, and adaptability. It concludes by describing the two main techniques of intrusion detection: anomaly detection, which flags deviations from a normal activity profile; and misuse detection, which searches for patterns matching known attacks.
The document proposes a security model for wireless sensor networks using zero knowledge protocol. It addresses security threats like cloning attacks, man-in-the-middle attacks, and replay attacks. The model uses a unique fingerprint for each node based on its neighboring nodes to detect cloning. It also uses zero knowledge protocol for sensor nodes to verify authenticity without transmitting cryptographic information, preventing man-in-the-middle and replay attacks. The paper analyzes the performance and security of the proposed model.
IRJET- An Intrusion Detection Framework based on Binary Classifiers Optimized...IRJET Journal
This document proposes an intrusion detection framework that uses multiple binary classifiers optimized by a genetic algorithm. It analyzes decision trees, naive Bayes, and support vector machines to classify network connections as normal or attacks based on the NSL-KDD dataset. The classifiers are aggregated and a genetic algorithm is used to generate high-quality solutions. Experimental results show that the proposed method achieves 99% accuracy in intrusion detection, outperforming single classification techniques. The goal is to develop an application that can efficiently process network data and identify intrusion risks.
A technical review and comparative analysis of machine learning techniques fo...IJECEIAES
Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network (Inception-CNN), Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning models in MANET.
This document outlines an agenda for discussing computer security policies and procedures. It covers topics such as introducing security policies, network architecture and security services, handling security incidents, ongoing security activities, and resources for security. The introduction defines key terms and outlines a basic approach to developing a security plan. It emphasizes identifying assets, threats, and implementing cost-effective protection measures.
Wireless Networks Security in Jordan: A Field StudyIJNSA Journal
- The document summarizes a study that evaluated the security of wireless networks in Jordan through a process called "wardriving" where the researchers drove around with wireless network detection tools.
- The results found that the majority (79.52%) of wireless networks tested were unsecured and vulnerable. Most networks used either low levels of encryption (68.67%) or no encryption at all (11.45%).
- Nearly all networks broadcast the default SSID (92.17%), leaving them exposed to potential hackers since changing the SSID is a basic security precaution.
Multi-Tiered Communication Security Schemes in Wireless Ad-Hoc Sensor NetworksIDES Editor
Networks of wireless micro-sensors for monitoring
physical environments have emerged as an important new
application area for wireless technology. Key attributes of
these new types of networked systems are the severely
constrained computational and energy resources and an ad
hoc operational environment. This paper is a study of the
communication security aspects of these networks. Resource
limitations and specific architecture of sensor networks call
for customized security mechanisms. Our approach is to
classify the types of data existing in sensor networks, and
identify possible communication security threats according
to that classification. We propose a communication security
scheme where for each type of data we define a corresponding
security mechanism. By employing this multi-tiered security
architecture where each mechanism has different resource
requirements, we allow for efficient resource management,
which is essential for wireless sensor networks.
This research proposal aims to develop autonomous post-intrusion network isolation systems using neural networks, rule-sets, and mathematical models. The research has four main goals: 1) investigate techniques to prevent comprehensive network infiltration if a system is compromised, 2) investigate proactive user auditing to mitigate fraud risk, 3) provide a model for network forensics after an intrusion, and 4) demonstrate a practical implementation. The methodology will include a literature review, mathematical modeling, analyzing isolation scenarios, and developing software. The research will be conducted over three semesters, with deliverables including a literature review, network isolation process, prototype architecture in the first semester, software development in the second, and testing/refinement in the
This document provides background information on the history and importance of network security. It discusses how the advent of the internet led to security becoming a major concern, as the internet's architecture allowed for many security threats. The document outlines the internet and network security timeline, from the creation of the ARPANET in 1969 to the crimes of Kevin Mitnick in the 1990s that heightened awareness of information security. It also examines the differences between data security and network security, and how a layered security model corresponds to the OSI model layers.
A comprehensive study on classification of passive intrusion and extrusion de...csandit
Cyber criminals compromise Integrity, Availability and Confidentiality of network resources in
cyber space and cause remote class intrusions such as U2R, R2L, DoS and probe/scan system
attacks .To handle these intrusions, Cyber Security uses three audit and monitoring systems
namely Intrusion Prevention Systems (IPS), Intrusion Detection Systems (IDS). Intrusion
Detection System (IDS) monitors only inbound traffic which is insufficient to prevent botnet
systems. A system to monitor outbound traffic is named as Extrusion Detection System (EDS).
Therefore a hybrid system should be designed to handle both inbound and outbound traffic.
Due to the increased false alarms preventive systems do not suite to an organizational network.
The goal of this paper is to devise a taxonomy for cyber security and study the existing methods
of Intrusion and Extrusion Detection systems based on three primary characteristics. The
metrics used to evaluate IDS and EDS are also presented.
Multi-stage secure clusterhead selection using discrete rule-set against unkn...IJECEIAES
The document discusses a proposed multi-stage secure clusterhead selection technique for wireless sensor networks using a discrete rule-set. The technique aims to securely select clusterheads during the data aggregation process and learn the nature of communications to gain knowledge about adversary intensity. It constructs primary and secondary rule-sets to filter and select secure clusterheads based on energy, neighbors, vulnerability, vicinity and distance from adversaries. Simulation results using MEMSIC sensor nodes showed the proposed approach reduces energy consumption and improves data delivery compared to existing methods.
This document discusses using data mining techniques to detect spyware. It begins by defining spyware and artificial intelligence. It then discusses three AI approaches that have been applied to spyware detection: heuristic technology, neural network technology, and data mining techniques. It focuses on using breadth-first search (BFS) within a data mining approach. The document finds that data mining techniques achieve an overall accuracy of 90.5% in detecting spyware, performing better than traditional signature-based or heuristic-based methods.
Using Machine Learning in Networks Intrusion Detection SystemsOmar Shaya
The internet and different computing devices from desktop computers to smartphones have raised many security and privacy concerns, and the need to automate systems that detect attacks on these networks has emerged in order to be able to protect these networks with scale. And while traditional intrusion detection methods may be able to detect previously known attacks, the issue of dealing with new unknown attacks arises and that brings machine learning as a strong candidate to solve these challenges.
In this report, we investigate the use of machine learning in detecting network attacks, intrusion detection, by looking at work that has been done in this field. Particularly we look at the work that has been done by Pasocal et al.
This document provides an overview of the features and capabilities of FortiGate IPS (Intrusion Prevention System), including over 7,000 signatures, DOS protection, deployment options, sensor functions, FortiGuard security services, performance metrics, packet logging, user quarantine, advanced NGIPS features, and DOS sensors. It also includes contact information for an Italian company specialized in Fortinet solutions.
CounterSnipe is an intrusion prevention system that protects networks from malware, unauthorized access, and vulnerabilities. It works in three steps: 1) collecting network information, 2) detecting unauthorized or malicious activity on the network, and 3) correlating what is detected with the network assets and taking actions like logging, blocking, and alerting. It integrates with existing infrastructure by deploying active protection appliances in different positions on the network. CounterSnipe is useful for any business with multiple computers and users on a network to help with compliance, protect web applications and networks, and monitor resource access policies.
The document discusses intrusion prevention systems (IPS), which monitor network and system activity to identify and block malicious activity. It describes how IPS uses signature-based or anomaly-based detection methods to identify intrusions. IPS can be network-based, host-based, wireless, or focus on network behavior analysis. The document contrasts IPS with intrusion detection systems (IDS), which can only detect and report intrusions, while IPS can actively prevent them. It also compares IPS to firewalls, noting that IPS monitors for unwanted entries while firewalls regulate activity based on set rules.
first ever presentation containing basic information about Intrusion Detection System and Intrusion Prevention System with advantages and disadvantages...
specially bibliography attached for engineering students.
it also contains 2013 powerpoint graphics.
hope it may helpful to u all.. your suggestions will be always welcomed..
This document provides an overview of intrusion prevention systems (IPS). It defines IPS and their main functions, which include identifying intrusions, logging information, attempting to block intrusions, and reporting them. It also discusses terminology related to IPS like false positives and negatives. The document outlines different detection methods used by IPS like signature-based, anomaly-based, and stateful protocol analysis. It categorizes IPS based on deployment like network-based, host-based, and wireless. It provides Snort, an open-source IPS, as a case study and discusses its components, rules structure, and challenges.
Network intrusion detection systems (NIDS) monitor network traffic for malicious activity by analyzing network packets at choke points like borders or the demilitarized zone. NIDS identify intrusions by comparing traffic patterns to known attack signatures or by detecting anomalies from established baselines. While NIDS can detect both previously known and unknown attacks, they require frequent signature database updates and may generate false positives. NIDS provide visibility without affecting network performance but cannot inspect encrypted traffic or all traffic on very large networks.
Intrusion detection and prevention systemNikhil Raj
This presentation describes how to implement Network based Intrusion Detection System (SNORT) in the network. Detecting and analyzing alerts generated and blocking the Attacker using Access Control List.
A brief discussion of network security and an introduction to cryptography. We end the presentation with a discussion of the RSA algorithm, and show how it works with a basic example.
Network Attack and Intrusion Prevention System Deris Stiawan
(1) The document discusses network attack and intrusion prevention systems. It describes how intrusion prevention systems (IPS) aim to detect and block threats in online traffic in real-time, beyond just detecting threats like intrusion detection systems (IDS).
(2) Feature extraction from network traffic is important for IPS to analyze without being overwhelmed by raw data. The document examines relevant features to monitor and criteria for deciding what is important to track.
(3) Experimental testing is needed to evaluate IPS performance. The document outlines stages for training systems, testing methodsologies, and resuming test results. This helps IPS avoid unexpected outcomes and ensures continuous monitoring.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document proposes a signature-based intrusion detection system using multithreading. It captures network packets and analyzes them for intrusions by comparing signatures to databases of known attacks. A multithreaded design is suggested to improve performance by processing packets in parallel threads. Agents would be deployed on the network with detection modules that use caching of frequent signatures to speed up analysis. An update module would transfer new frequent signatures to the caches.
Intrusion Detection System (IDS): Anomaly Detection using Outlier Detection A...Drjabez
This document describes a proposed approach for anomaly detection in intrusion detection systems using outlier detection. It begins with background on intrusion detection systems and issues with existing approaches. It then presents the proposed two-stage approach using outlier detection: 1) Training with large normal datasets in a distributed storage environment, and 2) Testing intrusion datasets to compute an error value compared to the trained model. If the error value exceeds a threshold, the test data is flagged as anomalous. Experimental results on network packet datasets demonstrate the approach can effectively identify anomalies.
COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTIONIJNSA Journal
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
Intrusion Detection Systems By Anamoly-Based Using Neural NetworkIOSR Journals
To improve network security different steps has been taken as size and importance of the network has
increases day by day. Then chances of a network attacks increases Network is mainly attacked by some
intrusions that are identified by network intrusion detection system. These intrusions are mainly present in data
packets and each packet has to scan for its detection. This paper works to develop a intrusion detection system
which utilizes the identity and signature of the intrusion for identifying different kinds of intrusions. As network
intrusion detection system need to be efficient enough that chance of false alarm generation should be less,
which means identifying as a intrusion but actually it is not an intrusion. Result obtained after analyzing this
system is quite good enough that nearly 90% of true alarms are generated. It detect intrusion for various
services like Dos, SSH, etc by neural network
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...ijsptm
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to
rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus
or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection
System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data
created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for
anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack
signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with
the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System
called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in
detecting abnormal content in the traffic data during information passing from one node to another and
also detects known attack signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
Network Intrusion Detection And Countermeasure Selection In Virtual Network (...ClaraZara1
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in detecting abnormal content in the traffic data during information passing from one node to another and also detects known attack signature and unknown attack. This approach is tested by running the artificial network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
This document discusses securing healthcare networks against cyber attacks. It proposes using intrusion detection systems to continuously monitor networks, firewalls to ensure endpoint devices comply with security policies, and biometrics for identity-based network access control. This would help protect patient privacy by safeguarding electronic health records and enhancing the security of hospital networks. The growing adoption of electronic records and devices in healthcare has increased risks of attacks that could intercept patient data or take over entire hospital networks. Strong network security measures are needed to address these risks.
IRJET- Implementation of Artificial Intelligence Methods to Curb Cyber Assaul...IRJET Journal
This document discusses how artificial intelligence methods can help curb cyber assaults. It reviews various AI techniques including expert systems, artificial neural networks, and intelligent agents that have been implemented or could potentially be implemented for cyber security purposes. For example, expert systems have been used to analyze risk levels on e-commerce sites and identify system vulnerabilities. Artificial neural networks have been applied for intrusion detection and classification of attacks. Intelligent agents are well-suited for combating cyber crimes due to their mobility, flexibility, and cooperative nature. The document concludes that while AI is already being used in various ways for cyber security, hackers may also start using AI techniques, presenting new challenges going forward.
Feature Selection using the Concept of Peafowl Mating in IDSIJCNCJournal
Cloud computing has high applicability as an Internet based service that relies on sharing computing resources. Cloud computing provides services that are Infrastructure based, Platform based and Software based. The popularity of this technology is due to its superb performance, high level of computing ability, low cost of services, scalability, availability and flexibility. The obtainability and openness of data in cloud environment make it vulnerable to the world of cyber-attacks. To detect the attacks Intrusion Detection System is used, that can identify the attacks and ensure information security. Such a coherent and proficient Intrusion Detection System is proposed in this paper to achieve higher certainty levels regarding safety in cloud environment. In this paper, the mating behavior of peafowl is incorporated into an optimization algorithm which in turn is used as a feature selection algorithm. The algorithm is used to reduce the huge size of cloud data so that the IDS can work efficiently on the cloud to detect intrusions. The proposed model has been experimented with NSL-KDD dataset as well as Kyoto dataset and have proved to be a better as well as an efficient IDS.
Feature Selection using the Concept of Peafowl Mating in IDSIJCNCJournal
Cloud computing has high applicability as an Internet based service that relies on sharing computing resources. Cloud computing provides services that are Infrastructure based, Platform based and Software based. The popularity of this technology is due to its superb performance, high level of computing ability, low cost of services, scalability, availability and flexibility. The obtainability and openness of data in cloud environment make it vulnerable to the world of cyber-attacks. To detect the attacks Intrusion Detection System is used, that can identify the attacks and ensure information security. Such a coherent and proficient Intrusion Detection System is proposed in this paper to achieve higher certainty levels regarding safety in cloud environment. In this paper, the mating behavior of peafowl is incorporated into an optimization algorithm which in turn is used as a feature selection algorithm. The algorithm is used to reduce the huge size of cloud data so that the IDS can work efficiently on the cloud to detect intrusions. The proposed model has been experimented with NSL-KDD dataset as well as Kyoto dataset and have proved to be a better as well as an efficient IDS.
Comparison study of machine learning classifiers to detect anomalies IJECEIAES
In this era of Internet ensuring the confidentiality, authentication and integrity of any resource exchanged over the net is the imperative. Presence of intrusion prevention techniques like strong password, firewalls etc. are not sufficient to monitor such voluminous network traffic as they can be breached easily. Existing signature based detection techniques like antivirus only offers protection against known attacks whose signatures are stored in the database.Thus, the need for real-time detection of aberrations is observed. Existing signature based detection techniques like antivirus only offers protection against known attacks whose signatures are stored in the database. Machine learning classifiers are implemented here to learn how the values of various fields like source bytes, destination bytes etc. in a network packet decides if the packet is compromised or not . Finally the accuracy of their detection is compared to choose the best suited classifier for this purpose. The outcome thus produced may be useful to offer real time detection while exchanging sensitive information such as credit card details.
The Practical Data Mining Model for Efficient IDS through Relational DatabasesIJRES Journal
Enterprise network information system is not only the platform for information sharing and information exchanging, but also the platform for enterprise production automation system and enterprise management system working together. As a result, the security defense of enterprise network information system does not only include information system network security and data security, but also include the security of network business running on information system network, which is the confidentiality, integrity, continuity and real-time of network business. Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements – they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, data mining-based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. Still, significant challenges exist in the design and implementation of production quality IDSs. Incrementing components such as data transformations, model deployment, and cooperative distributed detection remain a labor intensive and complex engineering endeavor. This paper describes DAID, a database-centric architecture that leverages data mining within the Relational RDBMS to address these challenges. DAID also offers numerous advantages in terms of scheduling capabilities, alert infrastructure, data analysis tools, security, scalability, and reliability. DAID is illustrated with an Intrusion Detection Center application prototype that leverages existing functionality in Relational Database 10g. Intrusion detection system work at many levels in the network fabric and are taking the concept of security to a whole new sphere by incorporating intelligence as a tool to protect networks against un-authorized intrusions and newer forms of attack. We have described formal model for the construction of network security situation measurement based on d-s evidence theory, frequent mode, and sequence model extracted from the data on network security situation based on the knowledge found method and convert the pattern on the related rules of the network security situation, and automatic generation of network security situation.
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...IJNSA Journal
With the ever increasing number and diverse type of attacks, including new and previously unseen attacks, the effectiveness of an Intrusion Detection System is very important. Hence there is high demand to reduce the threat level in networks to ensure the data and services offered by them to be more secure. In this paper we developed an effective test suite for improving the efficiency and accuracy of an intrusion detection system using the layered CRFs. We set up different types of checks at multiple levels in each layer. Our framework examines various attributes at every layer in order to effectively identify any breach of security. Once the attack is detected, it is intimated through mobile phone to the system administrator for safeguarding the server system. We established experimentally that the layered CRFs can thus be more effective in detecting intrusions when compared with the other previously known techniques.
When talk about intrusion, then it is pre- assume
that the intrusion is happened or it is stopped by the intrusion
detection system. This is all done through the process of collection
of network traffic information at certain point of networks in the
digital system. In this way the IDS perform their job to secure the
network. There are two types of Intrusion Detection: First is
Misuse based detection and second one is Anomaly based detection.
The detection which uses data set of known predefined set of
attacks is called Misuse - Based IDSs and Anomaly based IDSs are
capable of detecting new attacks which are not known to previous
data set of attacks and is based on some new heuristic methods. In
our hybrid IDS for computer network security we use Min-Min
algorithm with neural network in hybrid method for improving
performance of higher level of IDS in network. Data releasing is
the problem for privacy point of view, so we first evaluate training
for error from neural network regression state, after that we can get
outer sniffer by using Min length from source, so that we
hybridized as with Min – Min in neural network in hybrid system
which we proposed in our research paper
11.a genetic algorithm based elucidation for improving intrusion detection th...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection. The paper aims to reduce features from the KDD Cup 99 dataset and generate a rule set using genetic algorithms to detect intrusions. The genetic algorithm evolves rules over generations to maximize fitness. Experiments show this approach can improve detection rates and reduce false alarms compared to existing intrusion detection systems.
1.[1 9]a genetic algorithm based elucidation for improving intrusion detectio...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection by reducing features from the KDD 99 dataset. The genetic algorithm derives rules from the reduced training data and uses a fitness function to evaluate rule quality. The paper reviews related work applying genetic algorithms to intrusion detection. It describes the KDD 99 dataset used for training and testing. The proposed system incorporates a genetic algorithm and reduced feature set to detect and classify intrusions from normal network traffic.
1.[1 9]a genetic algorithm based elucidation for improving intrusion detectio...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection. The paper aims to reduce features from the KDD Cup 99 dataset and generate a rule set using genetic algorithms to detect intrusions with a condensed feature set. The genetic algorithm is used to evolve rules from the reduced training data, with a fitness function evaluating rule quality. Experiments and evaluations are conducted on the KDD Cup 99 dataset to test the proposed method.
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
This document proposes an intrusion detection system using customized rules for the Snort tool to improve security. The system uses Wireshark to scan network traffic for anomalies, Snort to detect attacks using customized rulesets for faster response times, and Wazuh and Splunk to analyze log files. Rules are created using the Snorpy tool and added to Snort to monitor for specific attacks like ICMP ping impersonation and authentication attempts. When attacks are attempted, the system successfully detects them and logs the alerts. The integration of these tools provides low-cost intrusion detection capabilities with automated threat identification and faster response compared to existing Snort configurations.
Sistem Deteksi Kegagalan pada Jaringan IoT dengan Menggunakan Metode Naive Ba...Deris Stiawan
Dokumen ini membahas sistem deteksi kegagalan pada jaringan IoT dengan menggunakan metode Naive Bayesian. Tujuannya adalah membuat sistem monitoring jaringan IoT dan menerapkan metode Naive Bayesian untuk mendeteksi kegagalan jaringan serta menampilkan grafik lalu lintas jaringan. Hasil pengujiannya menunjukkan metode ini dapat menentukan kondisi jaringan yaitu baik, normal atau rusak berdasarkan nilai latency dan packet loss
IoT : Peluang Riset di Bidang KesehatanDeris Stiawan
This document discusses opportunities for research in Internet of Things (IoT). It begins by defining IoT and describing common communication patterns. It then discusses areas of IoT including applications, device heterogeneity, taxonomy of technologies, healthcare, wellness, challenges, security and privacy issues. Examples are provided on hardware architecture, data collection, sensor placement, and monitoring in healthcare IoT. Drawbacks of existing security approaches are outlined. In closing, the author provides contact information for further discussion.
Klasifikasi Malware Trojan Ransomware Dengan Algoritma Support Vector Machine...Deris Stiawan
1. Penelitian ini mengklasifikasikan malware Trojan ransomware dan file normal menggunakan algoritma Support Vector Machine (SVM).
2. Hasil pengujian menunjukkan bahwa SVM kernel linier memberikan tingkat akurasi terbaik hingga 87,6% dibandingkan SVM Evolutionary.
3. Semakin besar jumlah data latih, semakin baik pula hasil akurasi yang dicapai.
Deteksi Serangan Black Hole dengan Metode Bayesian pada Mobile Ad Hoc NetworkDeris Stiawan
1. Penelitian ini menguji metode deteksi serangan black hole menggunakan Bayesian filtering pada jaringan MANET.
2. Hasil pengujian menunjukkan bahwa metode Bayesian mampu mendeteksi node black hole dan mencegah pengiriman data ke node tersebut.
3. Metode ini efektif mendeteksi serangan black hole tanpa memandang besar jaringan dan jumlah node black hole.
The document discusses trends and challenges in internet of things (IoT) from an information systems perspective. It describes IoT as involving the interconnection of heterogeneous networked entities through various communication patterns like human-to-human and machine-to-machine. The document outlines security and privacy as major issues in IoT due to the heterogeneity of devices, dynamicity of networks, and need to protect data. It reviews existing research that proposes solutions for these issues but identifies drawbacks like lack of testing on real heterogeneous devices and not addressing communication between different devices.
Dokumen tersebut membahas tentang peminatan riset di bidang COMNETS (Computer Network). Dokumen menjelaskan manfaat memilih peminatan riset COMNETS seperti mendapatkan pengalaman riset, belajar dari para ahli, dan berkesempatan untuk publikasi hasil penelitian. Dokumen juga menjelaskan tahapan umum dari suatu penelitian mulai dari pengumpulan data, pengolahan data, pengujian, hingga evaluasi hasil penelitian.
Perancangan Sistem Load Balancing Pada Web Server Dengan Algoritma Central Ma...Deris Stiawan
Tugas akhir ini membahas perancangan sistem load balancing pada web server dengan menggunakan algoritma Central Manager. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Central Manager dan Round Robin serta mengukur pembagian sumber daya pada web server secara real-time menggunakan algoritma Central Manager. Hasilnya menunjukkan bahwa algoritma Central Manager memiliki kecepatan respons yang lebih baik namun throughput yang lebih rendah dibandingkan Round Robin, serta mampu membagi
This document discusses trends and challenges related to the Internet of Things (IoT). It provides definitions and examples of key concepts in IoT including human-to-human, human-to-thing, machine-to-machine, and thing-to-thing communication patterns. The document also discusses issues around heterogeneity, dynamicity, and evolution in IoT networks. It describes some common security issues in IoT like denial of service attacks, eavesdropping, and physical damage. Additionally, it notes that privacy, security, and data hacking are major concerns for IoT and discusses the need for new legal approaches and suitable security solutions to guarantee confidentiality, access control, and privacy for users and devices.
This document provides instructions for hands-on exercises using the security testing tools in BackTrack 5. It discusses running a live BackTrack CD, configuring network settings, using Wireshark to capture network traffic, Nmap for scanning targets, Hydra for password cracking, and observing the results. Students are warned that any illegal activities outside the classroom are their full responsibility, and basic security best practices like keeping systems patched and using firewalls are recommended. The document concludes by providing questions to analyze the results of exercises using Nmap, Telnet, Wireshark, Hydra and TCPdump to observe network protocols, password cracking attempts, and the three-way handshake process between targets and hosts.
This document discusses network scanning and analysis tools. It describes using Nmap and Nikto to scan networks and web servers to find vulnerabilities. It also discusses using TCPdump and tshark to sniff network traffic. The main analysis tool discussed is Snort, which can analyze packets and recognize threats based on signatures in the Snort.conf file. Examples of signatures for threats like Code Red, Nimda, and directory traversal are provided.
This document provides instructions for hands-on exercises using Wireshark, a network protocol analyzer, to generate and analyze network traffic. Students are instructed to use Wireshark to capture and analyze traffic from pinging websites, browsing the web, transferring files, and mapping their network. They are asked to observe packet headers and payloads to understand protocols like IP, TCP, UDP, and ICMP. As a homework, students must draw and explain the three-way handshake process observed for different network activities. The goal is for students to gain practical experience analyzing network traffic using Wireshark.
This document discusses how ICT can be used to fight corruption in government. It provides examples of e-government systems and initiatives that several countries have implemented to increase transparency and reduce opportunities for corruption. These include putting rural property records online in India, restructuring the tax system in Pakistan to reduce direct citizen-official contact, and establishing e-procurement systems to prevent price fixing and allow public accountability. The document also outlines some of the challenges of implementing e-government systems, such as overcoming social, political, and infrastructure constraints. Overall, the document advocates for the use of ICT tools like e-government, e-procurement, and e-payment to enhance transparency, accountability, and anti-corruption goals in the public
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Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
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1. A SURVEY ON PREVENT NETWORK SYSTEM
Authors ;
Deris Stiawan, Ala’ Yaseen Ibrahim Shakhatreh,
Mohd. Yazid Idris, Kamarulnizam Abu Bakar,
Abdul Hanan Abdullah
Faculty of Computer Science & Information System
Universiti Teknologi Malaysia
2011
3. INTRODUCTION
Intrusion Detection was developed to identify and
report the attack in the late 1990s, as hackers’ attacks
and network worms began to affect the internet, it
detects hostile traffic, passive and sends alert but does
nothing to stop the attacks.
According to;
(Dacier & Wespi 1999), ( Zhang et al, 2003),
(Fuchsberger 2005), (Weinsberg et al. 2006),
(Shaikh et al. 2009), (Anuar et al. 2010),
4. Intrusion detection and intrusion response has the fundamental and
part of intrusion prevention mechanism in recent network security
challenge
(Stakhanova et al. 2007), (K. Salah & Kahtani 2010),
(Anuar et al. 2010),(Elshoush & Osman 2011)
early detection, protection and response system as an elementary of
IPS. Intrusion Response have function similar with IDS and part of it,
by maintaining detection, alerting and response to security operator.
Performed work by ;
(Manikopoulos 2003), (Zou & Towsley 2005), (Debar et al. 2008),
(Anuar et al. 2010), (Apel et al. 2010), (Mu et al. 2010) and
(Stakhanova et al. 2007)
5. (E. E. Schultz & Ray 2007) ;
Predicted the future of IPS technology, they prediction concerns on IPS
technology are very positive in market, as following ;
(i) better underlying intrusion detection,
(ii) advancement in application-level analysis,
(iii)more sophisticated response capabilities, and
(iv)integration of intrusion prevention into other security devices.
According to (E. Schultz 2004), has predicted IPSs have a bright future,
this technology will continue to be used by a growing number of
organisations to the point that it will become as a commonplace as
intrusion detection technology
(Shouman et al. 2010), describes superior characteristic of host based IPS
and use the term detection approach to show how IPS work.
6. IDS design just only identify
and examined to produce
alarm
IPS design is to enhance data processing
ability, intelligent, accurate of it self.
- Simple pattern matching
- Stateful pattern matching
-Protocol decode-based
analysis
- Heuristic-based analysis
- Recognize attack pattern
- Blocking action
- Stateful pattern matching
- Protocol decode-based analysis
- Heuristic-based analysis
- A passive security solution
- Detect attack only after they
have entered the network,
and do nothing to stop
attacks only just attacks
traffic and send alert to
trigger.
- Reactive response security solution
- Early Detection, proactive technique,
early prevent the attack, when an
attack is identified then blocks the
offending data
- Commonly collected in
source sensors
- Multisensory architectures
- Enable to integrated with other
platform
- Have the ability to integrate with
heterogeneous sensor
Usefulness
Signatures
Action
Activity
Sensor
I D S I P S
8. ISSUES & CHALLENGES
There are some
significant gaps ,
challenges and
preliminary result for
future direction in IPS
to improving, mining
and reducing false
alarm.
Data sets
Alert Management
Heterogeneous Data
Features Extraction
Minimizing False
Positives
Real-time Analyzer
Data Visualization
Unified Integration
Solution
Signatures
Traffic Volume
Design Topology
Logging
Defense IPS Devices
Sensor Management
Collaboration
(Stiawan, Abdullah, Yazid, 2010)
Deepali Virkar, Pune
Institute of computer
science (PICT), India Rozas, Institute
Teknologi Surabaya
Ramli, Institute
ICT, Bandung
10. Several reasons that required new data to be investigated
(i) the new model attack, more recently next application technologies are changing
the Web 2.0 security landscape, new attack pattern, and attack mechanism
(ii) the new emergence application, Web 2.0 applications are faced with all the threat
associated from past approach application, because of inherited traditional
resources in addition to new ones
(iii) there are new approaches (architecture and technology) in web technology
The experiment with new approach is urgently needed to get payload data
normal / attack and behavior activity user based on web 2.0 technology
shown classify interconnection behavior, it call habitual activity with number of connection of
activity user. We argues, from habitual activity, we can generate profiles of user behavior and
user profiles have be to be update periodically to include the most recent change frequently.
(Stiawan, Abdullah & Idris 2010) and (Dantu et al. 2009)
11. Alert Management
Alert management ability to cluster, merge, and correlate alerts. It is function
can able to recognize alert that correspond to the same occurrence of an attack.
Alert attributes consist of several fields that provide information about the
attack in stream network.
Alert correlation is defined as a process that
contains multiple components with the
purpose of analyzing alert and providing
high-level insight view on the security state
of the network under surveillance
(Ghorbani et al. 2010)
proposed collaborate IDS (CIDS), they used
centralized CIDS to correlated distributed
detection unit and alert management
correlation based on soft computing
(Elshoush & Osman 2011)
(W. Li & Tian 2010), to developed intrusion alerts
correlation system according to the alert
correlation approach using ontology-based
intrusion alerts.
(Sourour & Adel 2009), (Alsubhi et al. 2008),
proposed alert management module responsible
for collecting the alert generated from the self-
corrective IDS, this correlated with the alerts,
formulating & more general alert based TP.
12. From our observation, this alert information depends on the variant used or diversity
of format by different vendor product. Therefore, the solution for correlating alarm
with different vendor can solve with pre-process the message a common standard
data format
These solution leverages Intrusion Detection Message Exchange Format (IDME)
drafted by IETF Intrusion Detection Working Group based (IIDWG) on RFC 4766,
defines a data formats and exchange procedures for sharing information of interest to
intrusion detection and response systems, and to the management systems which
may need to interact with them (www.ietf.org/old/2009/ids.by.wg/idwg.html).
we can use defining a relationship metric between alert, the correlation may occur
because correlating alerts based on the similarity between alerts attributes, such as
time stamp, IP and ports addresses. Therefore, there are several issues that should be
addressed; aggregation of alert, knowledge-based, evaluation of alert and correlation
management
15. Effort work by
Saleznyov 2000,
Paula 2002, Depren 2005,
Hwang 2007, Chou 2009,
Aydin 2009
These approaches are not relevant
with currently behaviour access in
recently technology
Large of volume dataset and
multidimensional data has grown
rapidly in recent years.
Martin 2001 = CVE,
Varaarandi 2003 = Log file
Thakar 2005 = Honey pot
Tsang 2009 = blacklist users
Perdisci 2009 = IANA
Xuebiao 2009 = DNS traffic
Zhou 2010 = URL filtering
…It is possible to propose collecting
scattered information in routine
update regularly from provider or
security community. This data can be
useful information to be associated
with other.
Critical need of data analysis system
that can automatically analyse the
data to organise it and predict pattern
attack future trends.
16. is it possible for collecting and labeling
scattered information from security
services and community to identify
attack patterns and can predict it that
would occur?.
There are some problem to addresses
how to correlate heterogeneous event
parameters with different structure,
format, label and variable of data?
is it possible to provide threat identification, analysis and mitigation to continuously
provide the highest level using combination event parameters?.
propose data mining approach to collecting scattered information in routine update
regularly from provider or security community. This could be data from the web,
library data, logging, and past information that are stored as archives, these data can
form a pattern of specific information.
(Stiawan, Yazid, Abdullah. 2011)
17. Features Extraction
(David Nguyen, Gokhan Memik, Seda OgrenciMemik 2005),
(Gopi K. Kuchimanchi, Vir V. Phoha, Kiran S. Balagani 2004),
proposed in feature extraction as
an essential component in
anomaly detection to summarize
network behavior from a packet
stream.
proposed rough set theory to applied threat
assessments and classification method that
boundary between normal patterns and
abnormal, make it more suitable as a part of
this system
(Ye et al. 2010),
provided comprehensive review of the network traffic features and data preprocessing
techniques used by anomaly-based.
(Davis . 2011)
18. To recognized threat in real-traffic, feature extraction that must exist. Data from network traffic and
audit systems, which is for each type of data that needs to be examined (network packets, host
event / server farm logs, payload of data, etc) data preparation and feature extraction is currently a
challenging task.
How to enhance
recognized method
association rule mining,
outlier analysis, and
classification algorithms
in order to characterize
network behavior are
issues gap and
challenging from this
section.
19. To recognized threat in real-traffic, feature extraction that must exist. Data from network
traffic and audit systems, which is for each type of data that needs to be examined
(network packets, host event / server farm logs, payload of data, etc) data preparation and
feature extraction is currently a challenging task.
This caused in real traffic there are many packet data, audit data were manually inspected
to identify network traffic is impossible and was expensive, time-consuming, and inaccurate
due to the extremely large amount of audit data. Wherefore, solution for identify and
recognize security violation is urgently needed
How to enhance recognized method association rule mining, outlier analysis, and
classification algorithms in order to characterize network behavior are issues gap
and challenging from this section.
20. Minimizing False Alarm
evaluation accuracy
and speed has been
proposed by , they
were measured in
terms of FP and FN
with timelines activity
approaches
(Oh & Lee 2003),
Additionally, more
appropriately accurate
mechanism keeps the
number of false
negative and false
positive low
(Todd et al. 2007)
(Thakar et al. 2005), (Artail et al. 2006), (Ding et al. 2009), (Garcıa-Teodoro et al. 2009)
How to increase accurate with using clustering, percentage and distribute of sensor.
From this section, there are issues should be addresses, how to enhanced the
maturity method with new approach to adaptable from new threat and a new
method to increase of true alarm.
21. Real Time Analyzer
The system must be active and smart in classifying and distinguish of packet data, if curious or
mischievous are detected, alert is triggered and event response execute. This mechanism is
activated to terminate or allow process packet data associated with the event. Prevent attack
before entering the network by examining various data record and prevention demeanor of
pattern recognition.
respect from (J. Zhang et al. 2008), (Aydın et al. 2009), (Depren et al. 2005) work, they
present new approach for classification to identify threat. Unfortunately working in offline
mechanism, collecting data in real capturing but training and identify of threat is offline. In
the extension work (Hwang et al. 2007), has combined online and offline mechanism to
training data, cluster analysis, also attribute preprocessing.
One problem faced by all detection in IPS is that difficult to identify and recognized analysis of
packet in real-time traffic
Second problem is an accessing traffic can be more difficult then interpreting it,
network designer are built often performance, not visibility. They tend to be
concerned about how to best path destination packet, when carrying packet is
more important than analyzing them
22. According to Working in Real Time & box devices; (Weinsberg et al. 2006), presented IPS machine
based on Snort with pattern-matching algorithm to identify and recognize threat, previously in 2003
(Q. Zhang & Janakiraman 2003), produce DIPS with separate hardware based using field
programmable port extender.
23. Data Visualization
The continuous monitoring in graphical information for network operating center is
needed. During attack, there is a need for the security operator / officer to depict with
visualize the alert from sensor, fully managed and take necessary action respond to them.
24. According from (Steven Noel et al. 2009), they provide sophisticated attack graph
visualizations, with high-level overviews and detail drilldown, and work by
(Ren et al. 2007), (Alsaleh et al. 2008), (Read et al. 2009), has became a based literate
for develop visualization and network management in real network
There are some problems;
(i) collecting and managing data about networks and
their vulnerabilities,
(ii) building network attack models in terms of security
conditions and attacker exploits,
(iii) analyzing the models through simulated attacks to
produce attack graphs,
(iv) aggregating and filtering the attack graphs,
(v) drawing the graphs, and
(vi) providing interactive controls for attack graph
navigation
proposal work by
(S. Noel et al. 2005)
25. Conclusion
This paper has provided a comprehensive review of the early detection, response and
prevention system features. There are some issues and challenges in this area that can
be studied in future.
we argues the heterogeneous data has a signature update for predictor dataset,
feature extraction, real-time analyzer, and unified integration solution are essential
issues to be enhancement of learning phase. One integration system for detection,
prevention and reaction may still be valid today for network management,
countermeasure against, monitoring internal networks and for behavioral analysis
Furthermore, improvement with one stop solution system, testing and benchmarking
it with others in real-network traffic, will be made in future works.
We believe IPS could be an effective solution for building an integrated system in the
industrial world, by combining Firewall and IDS features with Network Management
for one integration system in Network Operating Center (NOC).