Evaluation of classification algorithms for intrusion detection in MANETs

Authors:
Sergio Pastrana;Aikaterini Mitrokotsa;Agustin Orfila;Pedro Peris-Lopez
Affiliations:
Computer Science Department, Carlos III University of Madrid, Leganes, Spain;Security and Cryptography Laboratory (LASEC), School of Computer and Communication Sciences, EPFL, Switzerland;Computer Science Department, Carlos III University of Madrid, Leganes, Spain;Computer Science Department, Carlos III University of Madrid, Leganes, Spain
Venue:
Knowledge-Based Systems
Year:
2012

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Abstract

Mobile Ad hoc Networks (MANETs) are wireless networks without fixed infrastructure based on the cooperation of independent mobile nodes. The proliferation of these networks and their use in critical scenarios (like battlefield communications or vehicular networks) require new security mechanisms and policies to guarantee the integrity, confidentiality and availability of the data transmitted. Intrusion Detection Systems used in wired networks are inappropriate in this kind of networks since different vulnerabilities may appear due to resource constraints of the participating nodes and the nature of the communication. This article presents a comparison of the effectiveness of six different classifiers to detect malicious activities in MANETs. Results show that Genetic Programming and Support Vector Machines may help considerably in detecting malicious activities in MANETs.