Abstract
DDoS attack detection has been given great attention in recent years. Some of these intrusion detection systems use statistical analysis method or wavelet transform method at the core, but they both suffer from the problem of high false alarm rate. In this paper, we apply the Multi-scale Principal Component Analysis (MSPCA) algorithm in the intrusion detection system which combines both the benefit of PCA and wavelet analysis. Then based on the behavior of DDoS attack, we generate several basic metrics to evaluate the proposed MSPCA based intrusion detection algorithm on the 1999 DARPA dataset. Our evaluation results show that our proposed MSPCA based algorithm performs better than the PCA based detection algorithm in terms of detection accuracy and false alarm rate with negligible computation overhead.
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