To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
JPJ1440 RRE: A Game-Theoretic Intrusion Response and Recovery Enginechennaijp
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
The document discusses the goals and methods of computer protection. It aims to control access to resources and prevent their unauthorized use. Hardware protection differentiates between user and monitor modes. The CPU protects modes through a mode bit, and switches to monitor for interrupts. Memory protection prevents processes from accessing memory not allocated to them. I/O protection makes I/O instructions privileged to monitor mode only. The goals are to share resources safely according to policies and minimize damage from misbehaving programs.
Security+ Guide to Network Security Fundamentals, 3rd Edition, by Mark Ciampa
Knowledge and skills required for Network Administrators and Information Technology professionals to be aware of security vulnerabilities, to implement security measures, to analyze an existing network environment in consideration of known security threats or risks, to defend against attacks or viruses, and to ensure data privacy and integrity. Terminology and procedures for implementation and configuration of security, including access control, authorization, encryption, packet filters, firewalls, and Virtual Private Networks (VPNs).
CNIT 120: Network Security
http://samsclass.info/120/120_S09.shtml#lecture
Policy: http://samsclass.info/policy_use.htm
Many thanks to Sam Bowne for allowing to publish these presentations.
Alert Analysis using Fuzzy Clustering and Artificial Neural NetworkIJRES Journal
Intrusion Detection System (IDS) is used to supervise all tricks which are running on particular machine or network. Also it will give you alert regarding to any attack. However now a day’s these alerts are very large in amount. It is very complicated to examine these attacks. We intend a time and space based alert analysis technique which can strap related alerts without surroundings knowledge and provide attack graph to help the administrator to understand the attack on host or network steps wise clearly and fittingly for analysis. A threat evaluation is given to discover out the most treacherous attack, which decrease administrator’s time and energy in calculating huge amount of alerts. We are analyzing the network traffic in form of attack using Entity Threat Evaluation (ETE) which find out which particular host is attacked, Gadget Threat Evaluation (GTE) which tells us within that host which device is attacked, Network Threat Evaluation (NTE) which tells us which network is attacked, Hit Threat Evaluation (HTE) by giving input as dataset of attack. Main idea is that the distribution of different types of attacks is not balanced. The attacks which are not repeatedly occurs, the learning sample size is too small as compared to high-frequent attacks. It makes Artificial Neural Network (ANN) not easy to become skilled at the characters of these attacks and therefore detection precision is much worse. To solve such troubles, we propose a new technique for ANN-based IDS, Fuzzy Clustering (FC-ANN), to enhance the detection precision for low-frequent attacks and detection stability.
The document discusses secure embedded systems as a requirement for cyber physical systems and the internet of things. It begins by providing examples of attacks on modern embedded systems like cars, industrial control systems, smart grids, and medical devices. It then discusses trends increasing security risks for embedded systems like network connectivity and standardization. Finally, it outlines requirements for future secure embedded systems and describes techniques like hardware security modules, secure elements, physical unclonable functions, and trusted operating systems to provide security in embedded systems going forward.
This document summarizes a technical seminar on security in embedded systems. It begins by defining an embedded system and network security. It then discusses reasons for hacking and a survey of security issues. It proposes both a hardware and hybrid hardware-software solution to security challenges and compares the proposed solutions to existing software solutions. Finally, it discusses future scopes like developing cryptographic chips and integrating security features into existing devices.
This document discusses intrusion detection systems (IDS). It covers the key components of an IDS, including methods of intrusion detection like audit trail processing, on-the-fly processing, profiles of normal behavior, signatures of abnormal behavior, and parameter pattern matching. The document also discusses building network-based IDS using tools like Snort and host-based IDS. It provides examples of labs to analyze network and wireless intrusion detection using machine learning techniques.
JPJ1440 RRE: A Game-Theoretic Intrusion Response and Recovery Enginechennaijp
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
The document discusses the goals and methods of computer protection. It aims to control access to resources and prevent their unauthorized use. Hardware protection differentiates between user and monitor modes. The CPU protects modes through a mode bit, and switches to monitor for interrupts. Memory protection prevents processes from accessing memory not allocated to them. I/O protection makes I/O instructions privileged to monitor mode only. The goals are to share resources safely according to policies and minimize damage from misbehaving programs.
Security+ Guide to Network Security Fundamentals, 3rd Edition, by Mark Ciampa
Knowledge and skills required for Network Administrators and Information Technology professionals to be aware of security vulnerabilities, to implement security measures, to analyze an existing network environment in consideration of known security threats or risks, to defend against attacks or viruses, and to ensure data privacy and integrity. Terminology and procedures for implementation and configuration of security, including access control, authorization, encryption, packet filters, firewalls, and Virtual Private Networks (VPNs).
CNIT 120: Network Security
http://samsclass.info/120/120_S09.shtml#lecture
Policy: http://samsclass.info/policy_use.htm
Many thanks to Sam Bowne for allowing to publish these presentations.
Alert Analysis using Fuzzy Clustering and Artificial Neural NetworkIJRES Journal
Intrusion Detection System (IDS) is used to supervise all tricks which are running on particular machine or network. Also it will give you alert regarding to any attack. However now a day’s these alerts are very large in amount. It is very complicated to examine these attacks. We intend a time and space based alert analysis technique which can strap related alerts without surroundings knowledge and provide attack graph to help the administrator to understand the attack on host or network steps wise clearly and fittingly for analysis. A threat evaluation is given to discover out the most treacherous attack, which decrease administrator’s time and energy in calculating huge amount of alerts. We are analyzing the network traffic in form of attack using Entity Threat Evaluation (ETE) which find out which particular host is attacked, Gadget Threat Evaluation (GTE) which tells us within that host which device is attacked, Network Threat Evaluation (NTE) which tells us which network is attacked, Hit Threat Evaluation (HTE) by giving input as dataset of attack. Main idea is that the distribution of different types of attacks is not balanced. The attacks which are not repeatedly occurs, the learning sample size is too small as compared to high-frequent attacks. It makes Artificial Neural Network (ANN) not easy to become skilled at the characters of these attacks and therefore detection precision is much worse. To solve such troubles, we propose a new technique for ANN-based IDS, Fuzzy Clustering (FC-ANN), to enhance the detection precision for low-frequent attacks and detection stability.
The document discusses secure embedded systems as a requirement for cyber physical systems and the internet of things. It begins by providing examples of attacks on modern embedded systems like cars, industrial control systems, smart grids, and medical devices. It then discusses trends increasing security risks for embedded systems like network connectivity and standardization. Finally, it outlines requirements for future secure embedded systems and describes techniques like hardware security modules, secure elements, physical unclonable functions, and trusted operating systems to provide security in embedded systems going forward.
This document summarizes a technical seminar on security in embedded systems. It begins by defining an embedded system and network security. It then discusses reasons for hacking and a survey of security issues. It proposes both a hardware and hybrid hardware-software solution to security challenges and compares the proposed solutions to existing software solutions. Finally, it discusses future scopes like developing cryptographic chips and integrating security features into existing devices.
This document discusses intrusion detection systems (IDS). It covers the key components of an IDS, including methods of intrusion detection like audit trail processing, on-the-fly processing, profiles of normal behavior, signatures of abnormal behavior, and parameter pattern matching. The document also discusses building network-based IDS using tools like Snort and host-based IDS. It provides examples of labs to analyze network and wireless intrusion detection using machine learning techniques.
This document discusses embedded systems security and how it can be improved. It is difficult to design secure embedded systems because economic incentives often reward producing insecure products, and adding security after development is challenging. However, security can be improved by designing it in from the start using principles like minimal implementation, component architecture, and independent validation. The document provides an overview of embedded systems, operating systems, networked devices, and motivates the importance of security.
Mca5042 cryptography and network securitysmumbahelp
This document provides information about obtaining fully solved SMU MBA Spring 2014 assignments. It provides contact information for a mail ID and phone number to send your semester and specialization name to receive assignments. It also includes an example assignment for the subject MCA5010 - Web Technology. The assignment includes 6 questions on topics like encryption algorithms, security models, IP security, and firewall types. Students are to send their details to the provided contact information to get assistance with solved assignments.
The document discusses several important operating system security issues. The operating system must provide protection mechanisms to prevent unauthorized access to processes and resources. It also needs flexibility to configure how resources are shared between processes and change these configurations as needed. Key security issues for an operating system include implementing protection mechanisms, controlling resource sharing, enforcing security policies, and utilizing authentication and authorization.
The overwhelming threat may be a challenge to
general security system. Fundamentally diverse alert and threat
techniques are been researched in order to reduce deceptive
warnings. Threat Detection Systems generates huge amount of
alerts which becomes challenging to deal with them and prepare
solution. The detection System checks inbound and outbound
network activities and finds an suspicious pattern that indicate
an ongoing steps for attack. Large amount of alert may contain
false alarm therefore need of alert analysis mechanisms to offer
high level information of seriousness of threat, how dangerous
device are and which device admin has to pay more attention. To
solve this query we would make use of time and space based alert
analysis technique that provides a solution in form of attack
graph and its evaluation that provides severity of attack to
administrator.
introduction to Embedded System SecurityAdel Barkam
The document provides an introduction to embedded system security. It defines an embedded system and gives examples. Embedded system security is defined as protecting resources an embedded system is responsible for. The document discusses why security is important for embedded systems and types of attacks, including embedded software attacks and embedded hardware attacks. It covers topics like firmware vs operating systems, and types of hardware attacks such as probing, side-channel attacks, and fault induction.
Situational awareness for computer network securitymmubashirkhan
This document discusses situational awareness, both traditional and cyber, and presents a scenario of a cyber attack. It introduces the Instance Based Learning Theory (IBLT) model for evaluating a security analyst's cyber situational awareness when responding to network events during an island hopping attack. Key factors that influence the analyst's decisions are described. The IBLT model represents situations, decisions, and utilities to model how analysts make judgments based on prior experiences stored in memory.
Optimized Intrusion Detection System using Deep Learning Algorithmijtsrd
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan
Seminar Presentation | Network Intrusion Detection using Supervised Machine L...Jowin John Chemban
By:
Jowin John Chemban (jowinchemban@gmail.com)
HGW16CS022 (2016-2020 Batch)
S7 B.Tech Computer Science Engineering
Holy Grace Academy of Engineering, Mala
Date : September 2019
Deep learning approach for network intrusion detection systemAvinash Kumar
This document presents a deep learning approach for network intrusion detection systems. It uses self-taught learning with sparse autoencoders for unsupervised feature learning followed by softmax regression for classification. This approach is tested on the NSL-KDD dataset and achieves over 98% accuracy on the training data and around 80% accuracy when classifying the separate test data into normal traffic and attacks. Future work involves implementing a real-time deep learning based NIDS and performing on-the-go feature learning directly from raw network traffic.
This document provides an overview of trusted computing concepts including:
- Defining security and how it can be violated through hardware and software flaws
- Explaining key terms like trust, trustworthy, and trusted computing
- Describing major trusted computing components like the endorsement key, sealed storage, remote attestation, and direct anonymous attestation
- Discussing issues around privacy, anonymity, and digital rights management in trusted computing systems
Least privilege, access control, operating system securityG Prachi
The document discusses principles of least privilege and access control concepts in operating system security. It defines security goals of confidentiality, integrity and availability known as the CIA triad. The principle of least privilege aims to limit a process's privileges to only those necessary for its execution. Access control concepts include discretionary access control where owners control access, and mandatory access control defined by security labels. A reference monitor provides complete mediation, is tamperproof, and verifiable to securely enforce access policies.
The document discusses operating system (OS) security. It begins by defining an OS and explaining that OS security refers to protecting information and data used on a computer system. It then lists some key OS security issues like physical security, authentication, software vulnerabilities, and malware. Several main security threats to OS are described such as unauthorized access, unauthorized resource use, data theft, and denial of service attacks. The document provides precautions to improve OS security like setting BIOS passwords, using strong user account passwords, encrypting data, installing antivirus software, and using a personal firewall. It states that Linux and other UNIX-based systems are generally more secure than Windows due to fewer viruses and malware. The conclusion emphasizes that security depends on the
This document summarizes a research paper about implementing self-healing mechanisms to protect against control flow attacks in wireless sensor networks. The paper proposes an access control scheme that can detect attempts to alter the control flow of sensor applications and then recover the sensor data. It processes application code at the machine instruction level rather than analyzing source code. The implementation shows that the self-healing scheme is lightweight and can effectively protect sensor applications from control flow attacks by enforcing access control, providing self-healing recovery, and diversifying code images across sensors.
Embedded system security is important to protect devices from attacks. Embedded systems are found in consumer electronics, industrial systems, vehicles and more. They need to be reliable but also secure due to limited resources. Hackers can exploit unprotected firmware to gain premium access or overclock devices. Hardware vulnerabilities like Meltdown and Spectre also affect embedded systems. Military equipment is at risk as well if systems are hacked. Attacks on embedded systems can be classified based on preconditions, vulnerabilities, targets, methods, and effects. Security needs to consider connectivity and manage devices effectively while protecting existing systems.
Application of machine learning and cognitive computing in intrusion detectio...Mahdi Hosseini Moghaddam
This document describes a proposed hardware-based machine learning intrusion detection system using cognitive processors. It discusses the need for new intrusion detection approaches due to limitations of signature-based methods. The proposed system collects network packet data using a Raspberry Pi and classifies it using a Cognimem CM1K cognitive processor chip, which implements restricted coulomb energy and k-nearest neighbor algorithms. The document outlines the system architecture, data collection and normalization methodology, and analysis of results from testing the CM1K chip on both custom and NSL-KDD network datasets, finding accuracy levels around 70-80% but slower processing times than a software simulation of the chip's algorithms. Future work areas include adding more packet features, using
Topics covered in this presentation:
What is an Embedded system ?
What are MISRA C rules ?
MISRA C conformance and deviations
Tools for MISRA C conformance
Embedded Security Rules
This document presents an overview of a project titled "Network Intrusion Detection and Countermeasure Selection in Virtual Network Systems". It discusses the need for effective intrusion detection and response systems to identify attacks in cloud systems and minimize their impact. It introduces NICE, a proposed multi-phase distributed mechanism that uses attack graph models and virtual network countermeasures. NICE includes network intrusion detection agents that monitor traffic and vulnerabilities. When threats are detected, countermeasures like packet inspection or virtual network reconfigurations are deployed.
This document proposes detecting Android malware using Long Short-Term Memory (LSTM) neural networks. It analyzes static and dynamic features from malware datasets to train and evaluate LSTM models. The LSTM models achieved better performance than static machine learning classifiers at detecting malware. Future work aims to apply LSTM to raw malware samples and study the internal mechanics of LSTM states to better understand how it carries application information across time steps.
Network Architecture review in context of Information security helps to understand how to actually review the components of network with respect to best practices.
IEEE 2014 JAVA PARALLEL DISTRIBUTED PROJECTS The client assignment problem fo...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 JAVA PARALLEL DISTRIBUTED PROJECTS Web service recommendation via e...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document discusses embedded systems security and how it can be improved. It is difficult to design secure embedded systems because economic incentives often reward producing insecure products, and adding security after development is challenging. However, security can be improved by designing it in from the start using principles like minimal implementation, component architecture, and independent validation. The document provides an overview of embedded systems, operating systems, networked devices, and motivates the importance of security.
Mca5042 cryptography and network securitysmumbahelp
This document provides information about obtaining fully solved SMU MBA Spring 2014 assignments. It provides contact information for a mail ID and phone number to send your semester and specialization name to receive assignments. It also includes an example assignment for the subject MCA5010 - Web Technology. The assignment includes 6 questions on topics like encryption algorithms, security models, IP security, and firewall types. Students are to send their details to the provided contact information to get assistance with solved assignments.
The document discusses several important operating system security issues. The operating system must provide protection mechanisms to prevent unauthorized access to processes and resources. It also needs flexibility to configure how resources are shared between processes and change these configurations as needed. Key security issues for an operating system include implementing protection mechanisms, controlling resource sharing, enforcing security policies, and utilizing authentication and authorization.
The overwhelming threat may be a challenge to
general security system. Fundamentally diverse alert and threat
techniques are been researched in order to reduce deceptive
warnings. Threat Detection Systems generates huge amount of
alerts which becomes challenging to deal with them and prepare
solution. The detection System checks inbound and outbound
network activities and finds an suspicious pattern that indicate
an ongoing steps for attack. Large amount of alert may contain
false alarm therefore need of alert analysis mechanisms to offer
high level information of seriousness of threat, how dangerous
device are and which device admin has to pay more attention. To
solve this query we would make use of time and space based alert
analysis technique that provides a solution in form of attack
graph and its evaluation that provides severity of attack to
administrator.
introduction to Embedded System SecurityAdel Barkam
The document provides an introduction to embedded system security. It defines an embedded system and gives examples. Embedded system security is defined as protecting resources an embedded system is responsible for. The document discusses why security is important for embedded systems and types of attacks, including embedded software attacks and embedded hardware attacks. It covers topics like firmware vs operating systems, and types of hardware attacks such as probing, side-channel attacks, and fault induction.
Situational awareness for computer network securitymmubashirkhan
This document discusses situational awareness, both traditional and cyber, and presents a scenario of a cyber attack. It introduces the Instance Based Learning Theory (IBLT) model for evaluating a security analyst's cyber situational awareness when responding to network events during an island hopping attack. Key factors that influence the analyst's decisions are described. The IBLT model represents situations, decisions, and utilities to model how analysts make judgments based on prior experiences stored in memory.
Optimized Intrusion Detection System using Deep Learning Algorithmijtsrd
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan
Seminar Presentation | Network Intrusion Detection using Supervised Machine L...Jowin John Chemban
By:
Jowin John Chemban (jowinchemban@gmail.com)
HGW16CS022 (2016-2020 Batch)
S7 B.Tech Computer Science Engineering
Holy Grace Academy of Engineering, Mala
Date : September 2019
Deep learning approach for network intrusion detection systemAvinash Kumar
This document presents a deep learning approach for network intrusion detection systems. It uses self-taught learning with sparse autoencoders for unsupervised feature learning followed by softmax regression for classification. This approach is tested on the NSL-KDD dataset and achieves over 98% accuracy on the training data and around 80% accuracy when classifying the separate test data into normal traffic and attacks. Future work involves implementing a real-time deep learning based NIDS and performing on-the-go feature learning directly from raw network traffic.
This document provides an overview of trusted computing concepts including:
- Defining security and how it can be violated through hardware and software flaws
- Explaining key terms like trust, trustworthy, and trusted computing
- Describing major trusted computing components like the endorsement key, sealed storage, remote attestation, and direct anonymous attestation
- Discussing issues around privacy, anonymity, and digital rights management in trusted computing systems
Least privilege, access control, operating system securityG Prachi
The document discusses principles of least privilege and access control concepts in operating system security. It defines security goals of confidentiality, integrity and availability known as the CIA triad. The principle of least privilege aims to limit a process's privileges to only those necessary for its execution. Access control concepts include discretionary access control where owners control access, and mandatory access control defined by security labels. A reference monitor provides complete mediation, is tamperproof, and verifiable to securely enforce access policies.
The document discusses operating system (OS) security. It begins by defining an OS and explaining that OS security refers to protecting information and data used on a computer system. It then lists some key OS security issues like physical security, authentication, software vulnerabilities, and malware. Several main security threats to OS are described such as unauthorized access, unauthorized resource use, data theft, and denial of service attacks. The document provides precautions to improve OS security like setting BIOS passwords, using strong user account passwords, encrypting data, installing antivirus software, and using a personal firewall. It states that Linux and other UNIX-based systems are generally more secure than Windows due to fewer viruses and malware. The conclusion emphasizes that security depends on the
This document summarizes a research paper about implementing self-healing mechanisms to protect against control flow attacks in wireless sensor networks. The paper proposes an access control scheme that can detect attempts to alter the control flow of sensor applications and then recover the sensor data. It processes application code at the machine instruction level rather than analyzing source code. The implementation shows that the self-healing scheme is lightweight and can effectively protect sensor applications from control flow attacks by enforcing access control, providing self-healing recovery, and diversifying code images across sensors.
Embedded system security is important to protect devices from attacks. Embedded systems are found in consumer electronics, industrial systems, vehicles and more. They need to be reliable but also secure due to limited resources. Hackers can exploit unprotected firmware to gain premium access or overclock devices. Hardware vulnerabilities like Meltdown and Spectre also affect embedded systems. Military equipment is at risk as well if systems are hacked. Attacks on embedded systems can be classified based on preconditions, vulnerabilities, targets, methods, and effects. Security needs to consider connectivity and manage devices effectively while protecting existing systems.
Application of machine learning and cognitive computing in intrusion detectio...Mahdi Hosseini Moghaddam
This document describes a proposed hardware-based machine learning intrusion detection system using cognitive processors. It discusses the need for new intrusion detection approaches due to limitations of signature-based methods. The proposed system collects network packet data using a Raspberry Pi and classifies it using a Cognimem CM1K cognitive processor chip, which implements restricted coulomb energy and k-nearest neighbor algorithms. The document outlines the system architecture, data collection and normalization methodology, and analysis of results from testing the CM1K chip on both custom and NSL-KDD network datasets, finding accuracy levels around 70-80% but slower processing times than a software simulation of the chip's algorithms. Future work areas include adding more packet features, using
Topics covered in this presentation:
What is an Embedded system ?
What are MISRA C rules ?
MISRA C conformance and deviations
Tools for MISRA C conformance
Embedded Security Rules
This document presents an overview of a project titled "Network Intrusion Detection and Countermeasure Selection in Virtual Network Systems". It discusses the need for effective intrusion detection and response systems to identify attacks in cloud systems and minimize their impact. It introduces NICE, a proposed multi-phase distributed mechanism that uses attack graph models and virtual network countermeasures. NICE includes network intrusion detection agents that monitor traffic and vulnerabilities. When threats are detected, countermeasures like packet inspection or virtual network reconfigurations are deployed.
This document proposes detecting Android malware using Long Short-Term Memory (LSTM) neural networks. It analyzes static and dynamic features from malware datasets to train and evaluate LSTM models. The LSTM models achieved better performance than static machine learning classifiers at detecting malware. Future work aims to apply LSTM to raw malware samples and study the internal mechanics of LSTM states to better understand how it carries application information across time steps.
Network Architecture review in context of Information security helps to understand how to actually review the components of network with respect to best practices.
IEEE 2014 JAVA PARALLEL DISTRIBUTED PROJECTS The client assignment problem fo...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 JAVA PARALLEL DISTRIBUTED PROJECTS Web service recommendation via e...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 DOTNET NETWORKING PROJECTS Network intrusion detection system using...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document discusses opinion mining and sentiment analysis. It begins by explaining how the rise of social media has created opportunities to understand public opinions on various topics by analyzing user comments. It then defines opinion mining as using computational techniques to extract, classify, understand and assess opinions expressed online, with sentiment analysis identifying sentiments in text. The document goes on to provide hardware and software requirements for a proposed system related to these techniques.
This document describes a proposed network intrusion detection system using attack behavior classification. The system aims to maximize recognition of network attacks by embedding their temporal behavior patterns into a neural network structure. It captures packets in real time using an engine that preprocesses data and sends it to modules for pattern recognition, classification, and generating alerts. The system was tested in a real environment and showed ability to detect attacks. It aims to address limitations of existing systems like constant monitoring overhead and inability to distinguish threats from normal traffic.
This document summarizes a presentation given by Rohan Nandi on security in embedded systems. The presentation covered what embedded systems are, an introduction to network security, why embedded system security is currently lacking and vulnerabilities. It also discussed countermeasures to avoid attacks, a proposed hardware-software solution, comparisons to existing software-only solutions, challenges, future scope, and references.
This document presents a multi-classification approach for detecting network attacks using a layered model. The proposed system consists of two stages - the first stage classifies network records as normal or an attack, while the second stage further classifies any detected attacks into four categories (DoS, Probe, R2L, U2R) using separate layers. Experimental results on the NSL-KDD dataset show the layered approach using the JRip classifier achieved very high classification accuracy of over 99% for each attack category, outperforming existing approaches. The multi-layered model is effective for improving detection of minority attack classes without reducing performance on majority classes.
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.
This document summarizes a technique for network threat detection and alarming using system statistics and support vector machines. It begins by introducing the importance of network security and common authentication and security mechanisms. It then provides an overview of threat detection systems and their purpose of identifying security incidents and producing reports. The rest of the document details various threat detection techniques, including expert systems, signature analysis, state-transition analysis, statistical analysis, user intention identification, machine learning, and data mining. It focuses on using statistical analysis of system variables like packet types, delay, drop rate, and buffer overflow to identify threat types like blackholes, wormholes, and flooding.
This document summarizes a proposed network attack alerting system that aims to reduce the large number of alerts generated by intrusion detection systems (IDS). The system uses both network-based and host-based IDS to detect attacks launched using the Backtrack attacking tools on a virtual network lab environment. Well-known open source security tools on the Security Onion Linux distribution are used to generate alerts. The system defines rules to identify important alert types and stores alerts in a database. It aims to eliminate redundant alerts for the same attack by analyzing attributes like source/destination IP and port. Alert severity levels are defined using threshold counts and times to classify alerts and help administrators respond appropriately.
This document summarizes a proposed network attack alerting system that aims to reduce redundant alerts from intrusion detection systems (IDS). The system uses both network-based and host-based IDS to detect attacks launched using the Backtrack penetration testing tool on a virtual network environment. Well-known open source IDS tools from the Security Onion distribution are used to generate alerts. The system builds a database of alerts and defines rules to eliminate duplicate alerts for the same attack based on attributes like source/destination IP and port. It also establishes a severity classification scheme using threshold values of alerts and time to help administrators prioritize responses.
This document describes a proposed system for detecting cyber attacks using Bayesian inference. It begins with an introduction to the problem of credit/debit card theft and existing physical unclonable functions. It then discusses the disadvantages of existing cyber attack detection systems, such as performance issues and high false positive rates. The proposed system builds a directed acyclic graph to represent the probability distribution of variables related to cyber attacks. It will use modules for data collection, preprocessing, model training/testing, and attack detection. The system will be implemented in Python using frameworks like Django and evaluated using algorithms like random forest, artificial neural networks, and support vector machines.
Replay of Malicious Traffic in Network TestbedsDETER-Project
In this paper we present tools and methods to integrate attack measurements from the Internet with controlled experimentation on a network testbed. We show that this approach provides greater fidelity than synthetic models. We compare the statistical properties of real-world attacks with synthetically generated constant bit rate attacks on the testbed. Our results indicate that trace replay provides fine time-scale details that may be absent in constant bit rate attacks. Additionally, we demonstrate the effectiveness of our approach to study new and emerging attacks. We replay an Internet attack captured by the LANDER system on the DETERLab testbed within two hours.
Data and tools from the paper are available at: http://montage.deterlab.net/magi/hst2013tools
Also read the LANDER Blog entry at: http://ant.isi.edu/blog/?p=411
I will talk about innovation in the area of cyber security analytics - developing machine learning methods to detect and block cyber attacks (e.g. detecting ransomware within 4 seconds of execution and killing the underlying processes). Rather than just focusing on this as a 'black box', I'll pull it apart and talk about how we can use these methods to enable security practitioners (SOC/CIRT etc) to ask and answer questions about 'what' and 'why' these methods are flagging attacks. I'll also talk about resilience of machine learning methods to manipulation and adversarial attacks - how stable these approaches are to diversity and evolution of malware for example.
HARDWARE SECURITY IN CASE OF SCAN-BASED ATTACK ON CRYPTO-HARDWAREVLSICS Design
The latest innovation technology in computing devices has given a rise of compact, speedy and economical products which also embeds cryptography hardware on-chip. This device generally holds secret key and confidential information, more attention has been given to attacks on hardware which guards such secure information. The attacker may leak secret information from symmetric crypto-hardware (AES, DES etc.) using side-channel analysis, fault injection or exploiting existing test infrastructure. This paper examines various DFT based attack implementation method applied to cryptographic hardware. The paper contains an extensive analysis of attacks based on various parameters. The countermeasures are classified and analyzed in details.
HARDWARE SECURITY IN CASE OF SCAN-BASED ATTACK ON CRYPTO-HARDWAREVLSICS Design
The latest innovation technology in computing devices has given a rise of compact, speedy and economical products which also embeds cryptography hardware on-chip. This device generally holds secret key and confidential information, more attention has been given to attacks on hardware which guards such secure information. The attacker may leak secret information from symmetric crypto-hardware (AES, DES etc.) using side-channel analysis, fault injection or exploiting existing test infrastructure. This paper examines various DFT based attack implementation method applied to cryptographic hardware. The paper contains an extensive analysis of attacks based on various parameters. The countermeasures are classified and analyzed in details.
HARDWARE SECURITY IN CASE OF SCAN-BASED ATTACK ON CRYPTO-HARDWAREVLSICS Design
The latest innovation technology in computing devices has given a rise of compact, speedy and economical
products which also embeds cryptography hardware on-chip. This device generally holds secret key and
confidential information, more attention has been given to attacks on hardware which guards such secure
information. The attacker may leak secret information from symmetric crypto-hardware (AES, DES etc.)
using side-channel analysis, fault injection or exploiting existing test infrastructure. This paper examines
various DFT based attack implementation method applied to cryptographic hardware. The paper contains
an extensive analysis of attacks based on various parameters. The countermeasures are classified and
analyzed in details
MACHINE LEARNING AND DEEP LEARNING MODEL-BASED DETECTION OF IOT BOTNET ATTACKS.IRJET Journal
This document discusses machine learning and deep learning models for detecting IoT botnet attacks. It begins with an abstract that outlines the challenges of securing the growing number of IoT devices and describes how machine learning and deep learning techniques like LSTM RNN can be used to develop effective detection systems. The introduction provides background on botnets, distributed denial of service attacks, and the need for detection systems. The literature review then summarizes several previous works that used techniques such as Bayesian classifiers, random neural networks, decision trees, and other machine learning algorithms for attack detection. The methodology section outlines the general approach of anomaly-based intrusion detection systems and different learning methods. The experimental setup describes collecting and preprocessing data, feature extraction, model training and evaluation
Secure intrusion detection and countermeasure selection in virtual system usi...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An Approach of Automatic Data Mining Algorithm for Intrusion Detection and P...IOSR Journals
This document summarizes an approach for using data mining algorithms to detect network intrusions and prevent security threats. It analyzes two datasets - one containing 997 records and another containing 11,438 records - using various classification algorithms in Weka to determine the best performing ones. The algorithms examined include PART, SMO, HyperPipes, Filtered Classifier, Random Forest, Naive Bayes Updateable and KStar. Classification rate and false positive rate are used to evaluate performance. The document also discusses related work on intrusion detection using neural networks, genetic algorithms and other approaches.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
An Overview of Cyber Attack and Computer Network Operations Si.docxnettletondevon
An Overview of Cyber Attack and Computer Network Operations Simulation
Sylvain P. Leblanc,
Andrew Partington
Computer Security Laboratory
Royal Military College of Canada
[email protected]
Ian Chapman,
Mélanie Bernier
Centre for Operational Research and Analysis
Defence Research and Development Canada
[email protected]
[email protected]
Keywords: Overview, Survey Paper, Cyber Attacks, Cyber
Warfare, Computer Network Operations
Abstract
This paper represents a snapshot of the current state of
the art in the simulation and modeling of cyber attacks and
defensive responses to those. It discusses a number of
simulations of cyber warfare, including live, virtual, and
constructive simulations. The simulations discussed in this
paper were found in the open literature and were conducted
in the private sector, academia, and government. Each
simulation is briefly described, including goals,
methodology, and a brief discussion of its accomplishments.
These modeling and simulation efforts are of particular
interest to the military modeling and simulation community,
as it is likely that military forces will continue to rely ever
more heavily on computer and communication networks.
1. INTRODUCTION
The concepts and technical challenges behind the
simulation of military conflicts in the traditional operational
domains – land, maritime, and air – have been well
understood for several decades, and thus numerous
applications have been developed to support computer
wargaming. These wargames are typically used to support
training and experimentation, and are seen as a safe and
cost-effective way to assess the effects of new technologies
and equipment before deploying them to the real battlefield.
Recent events, such as the 2007 cyber attack on
Estonia, have shown the rising importance of computer
network operations (CNO)
1
in an increasingly inter-
networked world. Both civilian and military domains have
become increasingly reliant on computer networks for
communication, information management, utilities
management, financial systems, air traffic control, and many
other critical applications. In fact, the authors argue
elsewhere at this conference that CNO education is vital for
both technical and non-technical commanders, and propose
using simulation to further these educational goals [1].
1
Per US Doctrine, CNO is comprised of Computer Network
Defense (CND), Computer Network Attack (CNA) and
Computer Network Exploitation (CNE). Many sources use
cyber warfare; we use both terms.
Cyber attacks have the potential to be extremely disruptive
to a wired society. To understand some of the ramifications
of these events, including their potential impact on the use
of networks, the research community has begun the
development of a number of applications to simulate cyber
warfare.
The paper is separated .
An Overview of Cyber Attack and Computer Network Operations Si.docxgalerussel59292
An Overview of Cyber Attack and Computer Network Operations Simulation
Sylvain P. Leblanc,
Andrew Partington
Computer Security Laboratory
Royal Military College of Canada
[email protected]
Ian Chapman,
Mélanie Bernier
Centre for Operational Research and Analysis
Defence Research and Development Canada
[email protected]
[email protected]
Keywords: Overview, Survey Paper, Cyber Attacks, Cyber
Warfare, Computer Network Operations
Abstract
This paper represents a snapshot of the current state of
the art in the simulation and modeling of cyber attacks and
defensive responses to those. It discusses a number of
simulations of cyber warfare, including live, virtual, and
constructive simulations. The simulations discussed in this
paper were found in the open literature and were conducted
in the private sector, academia, and government. Each
simulation is briefly described, including goals,
methodology, and a brief discussion of its accomplishments.
These modeling and simulation efforts are of particular
interest to the military modeling and simulation community,
as it is likely that military forces will continue to rely ever
more heavily on computer and communication networks.
1. INTRODUCTION
The concepts and technical challenges behind the
simulation of military conflicts in the traditional operational
domains – land, maritime, and air – have been well
understood for several decades, and thus numerous
applications have been developed to support computer
wargaming. These wargames are typically used to support
training and experimentation, and are seen as a safe and
cost-effective way to assess the effects of new technologies
and equipment before deploying them to the real battlefield.
Recent events, such as the 2007 cyber attack on
Estonia, have shown the rising importance of computer
network operations (CNO)
1
in an increasingly inter-
networked world. Both civilian and military domains have
become increasingly reliant on computer networks for
communication, information management, utilities
management, financial systems, air traffic control, and many
other critical applications. In fact, the authors argue
elsewhere at this conference that CNO education is vital for
both technical and non-technical commanders, and propose
using simulation to further these educational goals [1].
1
Per US Doctrine, CNO is comprised of Computer Network
Defense (CND), Computer Network Attack (CNA) and
Computer Network Exploitation (CNE). Many sources use
cyber warfare; we use both terms.
Cyber attacks have the potential to be extremely disruptive
to a wired society. To understand some of the ramifications
of these events, including their potential impact on the use
of networks, the research community has begun the
development of a number of applications to simulate cyber
warfare.
The paper is separated .
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
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- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
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- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
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- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
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Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
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https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
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Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
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Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
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detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
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Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
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Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
1. GLOBALSOFT TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
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RRE: A Game-Theoretic Intrusion Response and Recovery
Engine
ABSTRACT:
Preserving the availability and integrity of networked computing systems in the
face of fast-spreading intrusions requires advances not only in detection
algorithms, but also in automated response techniques. In this paper, we propose a
new approach to automated response called the response and recovery engine
(RRE). Our engine employs a game-theoretic response strategy against adversaries
modeled as opponents in a two-player Stackelberg stochastic game. The RRE
applies attack-response trees (ART) to analyze undesired system-level security
events within host computers and their countermeasures using Boolean logic to
combine lower level attack consequences. In addition, the RRE accounts for
uncertainties in intrusion detection alert notifications. The RRE then chooses
optimal response actions by solving a partially observable competitive Markov
decision process that is automatically derived from attack-response trees. To
support network-level multiobjective response selection and consider possibly
conflicting network security properties, we employ fuzzy logic theory to calculate
the network-level security metric values, i.e., security levels of the system’s current
2. and potentially future states in each stage of the game. In particular, inputs to the
network-level game-theoretic response selection engine, are first fed into the fuzzy
system that is in charge of a nonlinear inference and quantitative ranking of the
possible actions using its previously defined fuzzy rule set. Consequently, the
optimal network-level
response actions are chosen through a game-theoretic optimization process.
Experimental results show that the RRE, using Snort’s alerts, can protect large
networks for which attack-response trees have more than 500 nodes.
EXISTING SYSTEM:
The severity and number of intrusions on computer networks are rapidly
increasing. Generally, incident-handling techniques are categorized into three
broad classes. First, there are intrusion prevention methods that take actions to
prevent occurrence of attacks, for example, network flow encryption to prevent
man-in-the-middle attacks. Second, there are intrusion detection systems (IDSes),
such as Snort, which try to detect inappropriate, incorrect, or anomalous network
activities, for example, perceiving CrashIIS attacks by detecting malformed packet
payloads. Finally, There are intrusion response techniques that take responsive
actions based on received IDS alerts to stop attacks before they can cause
significant damage and to ensure safety of the computing environment. So far,
most research has focused on improving techniques for intrusion prevention and
detection, while intrusion response usually remains a manual process performed by
network administrators who are notified by IDS alerts and respond to the
intrusions. This manual response process inevitably introduces some delay
between notification and response,.
3. DISADVANTAGES OF EXISTING SYSTEM:
Which could be easily exploited by the attacker to achieve his or her
goal and significantly increase the damage.
To reduce the severity of attack damage resulting from delayed
response, an automated intrusion response is required that provides
instantaneous response to intrusion.
PROPOSED SYSTEM:
In this paper, we present an automated cost-sensitive intrusion response system
called the response and recovery engine (RRE) that models the security battle
between itself and the attacker as a multistep, sequential, hierarchical, non zero
sum, two-player stochastic game. In each step of the game, RRE leverages a new
extended attack tree structure, called the attack-response tree (ART), and received
IDS alerts to evaluate various security properties of the individual host systems
within the network. ARTs provide a formal way to describe host system security
based on possible intrusion and response scenarios for the attacker and response
engine, respectively. More importantly, ARTs enable RRE to consider inherent
uncertainties in alerts received from IDSes (i.e., false positive and false negative
rates), when estimating the system’s security and deciding on response actions.
Then, the RRE automatically converts the attack-response trees into partially
observable competitive Markov decision processes that are solved to find the
optimal response action against the attacker, in the sense that the maximum
4. discounted accumulative damage that the attacker can cause later in the game is
minimized.
ADVANTAGES OF PROPOSED SYSTEM:
Improves its scalability for large-scale computer networks, in which RRE is
supposed to protect a large number of host computers against malicious
attackers.
Finally, separation of high- and low-level security issues significantly
simplifies the accurate design of response engines.
SYSTEM ARCHITECTURE:
5. SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 512 Mb.
6. SOFTWARE REQUIREMENTS:
Operating system : Windows XP/7.
Coding Language : JAVA/J2EE
IDE : Netbeans 7.4
Database : MYSQL
REFERENCE:
Saman A. Zonouz, Himanshu Khurana, William H. Sanders, and Timothy M.
Yardley “RRE: A Game-Theoretic Intrusion Response and Recovery Engine”
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,
VOL. 25, NO. 2, FEBRUARY 2014