2008/07/13 by Aikaterini Mitrokotsa, Mitrokotsa, Aikaterini, Manolis Tsagkaris +3
Computer Science · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Internet Traffic Analysis and Secure E-voting #Mobile Ad Hoc Networks #Network Security and Intrusion Detection #Networking and Internet Architecture (cs.NI) #cs.CR #cs.NI
paper · pdf · doi:10.48550/arxiv.0807.2049
12 pages, 7 figures, presented at MedHocNet 2008
arxiv created 2008/07/13 · openalex publication_date 2008/07/13 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we present the design and evaluation of intrusion detection models for MANETs using supervised classification algorithms. Specifically, we evaluate the performance of the MultiLayer Perceptron (MLP), the Linear classifier, the Gaussian Mixture Model (GMM), the Naive Bayes classifier and the Support Vector Machine (SVM). The performance of the classification algorithms is evaluated under different traffic conditions and mobility patterns for the Black Hole, Forging, Packet Dropping, and Flooding attacks. The results indicate that Support Vector Machines exhibit high accuracy for almost all simulated attacks and that Packet Dropping is the hardest attack to detect.