CARRADS: Cross layer based adaptive real-time routing attack detection system for MANETS

Authors:
John Felix Charles Joseph;Amitabha Das;Bu-Sung Lee;Boon-Chong Seet
Affiliations:
Center for Multimedia and Networks (CeMNet), Division of Computer Communications, School of Computer Science and Engineering, Nanyang Technological University, Singapore;Convergence Lab, SETLabs, Infosys Technologies Ltd., Bangalore, India;Center for Multimedia and Networks (CeMNet), Division of Computer Communications, School of Computer Science and Engineering, Nanyang Technological University, Singapore;Department of Electrical and Electronics Engineering, Auckland University of Technology, New Zealand
Venue:
Computer Networks: The International Journal of Computer and Telecommunications Networking
Year:
2010

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Abstract

Routing behavior in ad hoc networks is highly transient. Thus, dynamically adapting the routing attack detection system at real-time to new attacks and changing network conditions is critical in ad hoc networks. Conventional incremental learning methods are computationally expensive for resource-constrained nodes in ad hoc networks. In this paper, we propose CARRADS, a computationally efficient methodology for adapting the intrusion detection model at real-time. The adaptation process consists of two major stages. In the first stage, the main task is to identify occurrence of new patterns in the routing control traffic and prioritize them based on their information content. The second stage of adaptation is to incrementally update the detection model using the new patterns with minimum computational overhead. CARRADS uses SVM algorithm for its superior detection abilities. However, using some innovative techniques the computational overhead of incremental update is reduced by a factor of 20 to 30 times at the cost of a negligible decrease in detection accuracy. This makes CARRADS a viable approach for real-time IDS in ad hoc networks.