RT Journal Article
T1 Evaluation of Classification Algorithms for Intrusion Detection in MANETs
A1 Pastrana, Sergio
A1 Mitrokotsa, Aikaterini
A1 Orfila, Agustín
A1 Peris López, Pedro
AB 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.
PB Elsevier
SN 0950-7051
YR 2012
FD 2012-12
LK https://hdl.handle.net/10016/14984
UL https://hdl.handle.net/10016/14984
LA eng
NO This work has been partially supported by the Marie Curie IEF, project"PPIDR: Privacy-Preserving Intrusion Detection and Response in WirelessCommunications", grant number 252323, and also by the Comunidad deMadrid and Carlos III University of Madrid, Project EVADIR CCG10-UC3M/TIC-5570.
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RD 19 may. 2024