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General Information
    • ISSN: 1793-8244 (Print)
    • Abbreviated Title:  J. Adv. Comput. Netw.
    • Frequency: Semiyearly
    • DOI: 10.18178/JACN
    • Editor-in-Chief: Professor Haklin Kimm
    • Executive Editor: Ms. Cherry Chan
    • Abstracting/ Indexing: EBSCO, ProQuest, and Google Scholar.
    • E-mail: jacn@ejournal.net
    • APC: 500USD
Professor Haklin Kimm
East Stroudsburg University, USA
I'm happy to take on the position of editor in chief of JACN. We encourage authors to submit papers on all aspects of computer networks.

JACN 2015 Vol.3(2): 93-98 ISSN: 1793-8244
DOI: 10.7763/JACN.2015.V3.148

Multi-class Intrusion Detection System for MANETs

Konagala Pavani and Auvula Damodaram

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).


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.

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