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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Toward Lightweight Detection and Visualization for Denial of Service Attacks
verfasst von : Dong Seong Kim, Sang Min Lee, Jong Sou Park
Erschienen in: MICAI 2006: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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In this paper, we present a lightweight detection and visualization methodology for Denial of Service (DoS) attacks. First, we propose a new approach based on Random Forest (RF) to detect DoS attacks. The classification accuracy of RF is comparable to that of Support Vector Machines (SVM). RF is also able to produce the importance value of individual feature. We adopt RF to select intrinsic important features for detecting DoS attacks in a lightweight way. And then, with selected features, we plot both DoS attacks and normal traffics in 2 dimensional space using Multi-Dimensional Scaling (MDS). The visualization results show that simple MDS can help one to visualize DoS attacks without any expert domain knowledge. The experimental results on the KDD 1999 intrusion detection dataset validate the possibility of our approach.