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|Title||Hacker Location Detection System (HLDS) for IPv6 Routers|
The Enhanced IPv6-next generation routers and IPv6-network architectures are two of the most important aspects of the Internet today. The users’ demand of new and more complex services, as well as significant constraints imposed by current technologies, make the design of IPv6 next-generation routers hard and challenging. IPv6 next-generation router architectures are expected to provide capabilities, such as availability, reliability, modularity and performance scalability, as well as the support of antivirus, anti spam, and antispyware, which spans from energy-efficiency requirements to advanced levels of flexibility and programmability. In this thesis we address the problem of network monitoring and automatic detection of vulnerabilities, weaknesses, and shortcomings of IPv4-based networks. Our main objective is to generate Software Router Plug-ins (SRPs) that will be deployed as advanced and flexible tools to IPv6 next generation routers to increase the network security. SRPs are one of the most intelligent anti-malware technique that we will be used in IPv6 routers. We propose an original methodology called Hacker Location Detection System (HLDS). HLDS is capable of both detection of hackers and identification of their location based on their IP address, MAC address, and geographic location. HLDS is a Software Router Plug-in (SRP) that will be used in IPv6 routers to provide Internet security. Since hackers are able to change IP and MAC addresses of their computers and NIC respectively, our proposed system does not depend only on IP or MAC addresses but also on location of hackers. Hence, even if hackers change these IP and/or MAC addresses our proposed system is capable of tracing these hackers. We validate the proposed HLDS by developing experiments to measure its performance. We used some performance metrics to measure the performance of HLDS which include accuracy, False positive rate, and False negative rate. We also compare the performance results of HLDS with performance of similar tools in the literature, namely Hierarchical SOM, IDS using SVM, and Adaboost with Decision tree. Performance results show that HLDS has a better accuracy, less false positive rate, and less false negative rate than these programs.
|Publisher||the islamic university|
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