Abstract—As MANETs change their topology dynamically,
intrusion detection in these networks is a challenging task. These
networks are more liable to the security attacks because of the
properties such as node mobility, lack of concentration points
where aggregated traffic can be analyzed, intermittent wireless
communications and limited band width. We present a multiclass
intrusion detection system that addresses these challenges.
In this paper we propose a neural network method based on
MLP (multi-layer perceptron) for detecting normal and
attacked behavior of the system. The method was tested for
Black Hole and Gray Hole attacks. We have implemented these
attacks using NS2 simulator. The method successfully detected
these attacks. We compared the results with KNN (K-Nearest
Neighborhood) which is another classifier used for classification.
Finally, Re sampling methods were also applied to assess the
performance of classifier. This paper presents a graphical
representation of the results.
Index Terms—Intrusion detection system, Black Hole attack,
Gray Hole attack, multi-layer perceptron, K-nearest
neighborhood.
The authors are with Vaagdevi College of Engineering/CSE, Warangal,
India (e-mail: bandaripavani@gmail.com).
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Cite:Konagala Pavani and Auvula Damodaram, "Multi-class Intrusion Detection System for MANETs," Journal of Advances in Computer Networks vol. 3, no. 2, pp. 93-98, 2015.
