NDLI: Misbehavior detection in Mobile ad hoc Networks using Artificial Immune System approach

Content Provider	IEEE Xplore Digital Library
Author	Ansari, M.S.A. Inamullah, M.
Copyright Year	2011
Description	Author affiliation: Department of Computer Engineering, Aligarh Muslim University, Aligarh, India (Ansari, M.S.A.; Inamullah, M.)
Abstract	Application of Artificial Immune System (AIS) evolves as a key Artificial Intelligence concept for detecting misbehavior, ensuring security, detecting faults and performing data mining in Mobile ad hoc Networks (MANETs). Recognition of misbehaving nodes is a must for proper functioning of a MANET. AIS approach has a unique feature of learning which is absent in other techniques (e.g. reputation system). Danger Signal and Clonal Selection are the key techniques of AIS for misbehavior detection. Different types of misbehavior occur in MANETs, and then at different network layers-Physical, Data Link, and Network. In this paper we investigate and detect misbehavior at Network Layer. We performed experiment using the concepts of danger signal and clonal selection of AIS. Result of the misbehavior detection system depends on the way we use the danger signal for misbehavior detection. We propose an enhancement in the misbehavior detection using proper handling of danger signal. We compare our proposed concept of misbehavior detection with existing concepts. We show the experimental results showing the improvement in performance of AIS for misbehavior detection.
Starting Page	1
Ending Page	6
File Size	306771
Page Count	6
File Format	PDF
ISBN	9781467300933
ISSN	21531676
e-ISBN	9781467300940
DOI	10.1109/ANTS.2011.6163683
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-18
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	MANET DSR Danger Signal Bones Routing Clonal selection Ad hoc networks Routing protocols AIS Ns2 Mobile computing Immune system
Content Type	Text
Resource Type	Article

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