arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 0807.2049cs.CRcs.NI

Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms

Aikaterini Mitrokotsa, Manolis Tsagkaris, Christos Douligeris

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑