Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/13414
Title: Efficient approaches to improve the performance of anomaly intrusion detection in wireless networks using mac layer and network layer feature set
Researcher: Kishoreraja, P.C.
Guide(s): Suganthi, M.
Keywords: Anomaly, intrusion, wireless networks, mac layer, network layer, detection techniques
Upload Date: 28-Nov-2013
University: Anna University
Completed Date: 
Abstract: Wireless ad-hoc network is an autonomous system of wireless nodes connected by wireless links. The self-organizing property of wireless ad hoc network provides an extremely flexible method for establishing communications in situations where geographical or terrestrial constraints demand totally distributed network, such as battlefields, emergency and disaster areas. This thesis explores various types of intrusion detection techniques such as misuse intrusion detection and anomaly intrusion detection for wireless ad-hoc networks. The design of anomaly intrusion detection technique for wireless ad-hoc network has been proposed in this research work. This research work focuses on wireless node behavior based detection technique. Most of anomaly intrusion detection systems are focusing on upper layers traffic to profile normal behavior of wireless node. This research work focus on only MAC and network layer of wireless node. This research work introduces three behavioral indexes. They are match index, entropy index, and newness index. This research work proposes threshold based detection technique for three behavioral indexes. To improve further the performance of anomaly intrusion detection for wireless ad hoc network, this research introduces combined behavior index and also introduces one more behavior index called sequence index which checks consistency of wireless node feature set that adds to the combined behavioral index. In the implementation part, wireless network traffic is extracted using ns2 simulator. Algorithm for anomaly intrusion detection using genetic algorithm is developed using C language under Linux platform. Sequence behavior Index is separately developed using PERL language under Linux platform. The performance of anomaly intrusion detection is analyzed using single behavioral indices, combined index and combined index with sequence index. The results demonstrate that combined index shows better detection rate and low false alarm rate. newline newline newline
Pagination: 20, 124
URI: http://hdl.handle.net/10603/13414
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File83.84 kBAdobe PDFView/Open
02_certificates.pdf1.24 MBAdobe PDFView/Open
03_abstract.pdf47.48 kBAdobe PDFView/Open
04_acknowledgement.pdf51.53 kBAdobe PDFView/Open
05_contents.pdf109 kBAdobe PDFView/Open
06_chapter 1.pdf129.83 kBAdobe PDFView/Open
07_chapter 2.pdf168.06 kBAdobe PDFView/Open
08_chapter 3.pdf158.3 kBAdobe PDFView/Open
09_chapter 4.pdf948.89 kBAdobe PDFView/Open
10_chapter 5.pdf260.68 kBAdobe PDFView/Open
11_chapter 6.pdf71.73 kBAdobe PDFView/Open
12_references.pdf115.75 kBAdobe PDFView/Open
13_publications.pdf55.13 kBAdobe PDFView/Open
14_vitae.pdf44.51 kBAdobe PDFView/Open


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