Artificial Immune System Based MAC Layer Misbehavior Detection in MANET

MAC layer misbehavior drastically degrades the network efficiency even in the presence of secure ad hoc routing protocols. Even small number of malicious nodes may cause network partitioning or lead to failure of whole network. Simple attacks such as jamming or disruption on the 802.11 MAC, protocol if not taken care properly, propagated to the network layer. Detecting misbehaving node and punishing them is the only way for network survival. This paper introduces a Misbehavior Detection System (MDS) for MANET based on Artificial Immune System (AIS). Negative Selection technique is used for generating the detectors for identifying deviation from normal behavior. The proposed system detects malicious and selfish nodes performing misbehavior at MAC layer with the ability of learning and detecting new misbehavior. The system performance is evaluated using network simulator NS2 for MANET MAC layer 802.11 protocols over two on demand routing protocols AODV and DSR. Detection rate, false positive rate and Packet delivery rate are used as metrics for evaluation.

[1]  Fabio A. González,et al.  Anomaly Detection Using Real-Valued Negative Selection , 2003, Genetic Programming and Evolvable Machines.

[2]  S. M. Ramesh,et al.  Biologically inspired artificial intrusion detection system for detecting wormhole attack in MANET , 2014, Wirel. Networks.

[3]  Fabio A. González,et al.  An immunity-based technique to characterize intrusions in computer networks , 2002, IEEE Trans. Evol. Comput..

[4]  Stephanie Forrest,et al.  An immunological model of distributed detection and its application to computer security , 1999 .

[5]  Jie Wu,et al.  A Survey on Intrusion Detection in Mobile Ad Hoc Networks , 2007 .

[6]  Stephanie Forrest,et al.  Immunity by design: an artificial immune system , 1999 .

[7]  Leandro Nunes de Castro,et al.  Artificial Immune Systems: A Novel Approach to Pattern Recognition , 2002 .

[8]  Jean-Yves Le Boudec,et al.  An Artificial Immune System Approach to Misbehavior Detection in Mobile Ad Hoc Networks , 2004, BioADIT.

[9]  Md Shamsher Alam Ansari,et al.  Misbehavior detection in Mobile ad hoc Networks using Artificial Immune System approach , 2011, 2011 Fifth IEEE International Conference on Advanced Telecommunication Systems and Networks (ANTS).

[10]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[11]  Teerawat Issariyakul,et al.  Introduction to Network Simulator NS2 , 2008 .

[12]  Stephanie Forrest,et al.  Architecture for an Artificial Immune System , 2000, Evolutionary Computation.

[13]  Peter J. Bentley,et al.  An evaluation of negative selection in an artificial immune system for network intrusion detection , 2001 .

[14]  David E. Goldberg,et al.  FOX-GA: A Genetic Algorithm for Generating and Analyzing Battlefield Courses of Action , 1999, Evolutionary Computation.