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Cross-Layer Detection of Sinking Behavior in Wireless Ad Hoc Networks Using SVM and FDA
March/April 2011 (vol. 8 no. 2)
pp. 233-245
ASCII Text
x 
 
John Felix Charles Joseph, Bu-Sung Lee, Amitabha Das, Boon-Chong Seet, "Cross-Layer Detection of Sinking Behavior in Wireless Ad Hoc Networks Using SVM and FDA," IEEE Transactions on Dependable and Secure Computing, vol. 8, no. 2, pp. 233-245, March/April, 2011.
 
 
BibTex
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@article{ 10.1109/TDSC.2009.48,
author = {John Felix Charles Joseph and Bu-Sung Lee and Amitabha Das and Boon-Chong Seet},
title = {Cross-Layer Detection of Sinking Behavior in Wireless Ad Hoc Networks Using SVM and FDA},
journal ={IEEE Transactions on Dependable and Secure Computing},
volume = {8},
number = {2},
issn = {1545-5971},
year = {2011},
pages = {233-245},
doi = {http://doi.ieeecomputersociety.org/10.1109/TDSC.2009.48},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Dependable and Secure Computing
TI - Cross-Layer Detection of Sinking Behavior in Wireless Ad Hoc Networks Using SVM and FDA
IS - 2
SN - 1545-5971
SP233
EP245
EPD - 233-245
A1 - John Felix Charles Joseph,
A1 - Bu-Sung Lee,
A1 - Amitabha Das,
A1 - Boon-Chong Seet,
PY - 2011
KW - Cross-layer design
KW - routing attacks
KW - ad hoc networks
KW - intrusion detection
KW - sinking.
VL - 8
JA - IEEE Transactions on Dependable and Secure Computing
ER -
 
John Felix Charles Joseph, Nanyang Technological University, Singapore
Bu-Sung Lee, Nanyang Technological University, Singapore
Amitabha Das, Infosys Technologies Ltd., Bangalore
Boon-Chong Seet, Auckland University of Technology, Auckland
The uniqueness of security vulnerabilities in ad hoc networks has given rise to the need for designing novel intrusion detection algorithms, different from those present in conventional networks. In this work, we propose an autonomous host-based intrusion detection system for detecting malicious sinking behavior. The proposed detection system maximizes the detection accuracy by using cross-layer features to define a routing behavior. For learning and adaptation to new attack scenarios and network environments, two machine learning techniques are utilized. Support Vector Machines (SVMs) and Fisher Discriminant Analysis (FDA) are used together to exploit the better accuracy of SVM and faster speed of FDA. Instead of using all cross-layer features, features from MAC layer are associated/correlated with features from other layers, thereby reducing the feature set without reducing the information content. Various experiments are conducted with varying network conditions and malicious node behavior. The effects of factors such as mobility, traffic density, and the packet drop ratios of the malicious nodes are analyzed. Experiments based on simulation show that the proposed cross-layer approach aided by a combination of SVM and FDA performs significantly better than other existing approaches.

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Index Terms:
Cross-layer design, routing attacks, ad hoc networks, intrusion detection, sinking.
Citation:
John Felix Charles Joseph, Bu-Sung Lee, Amitabha Das, Boon-Chong Seet, "Cross-Layer Detection of Sinking Behavior in Wireless Ad Hoc Networks Using SVM and FDA," IEEE Transactions on Dependable and Secure Computing, vol. 8, no. 2, pp. 233-245, March-April 2011, doi:10.1109/TDSC.2009.48
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