App-DDoS detection method based on K-means multiple principal component analysis
Aiming at the application layer distributed deny of service(App-DDoS) attacks, a K-means multiple principal component analysis algorithm(KMPCAA) utilizing the Web log mining was proposed, then an App-DDoS detection method based on KMPCAA was presented. Firstly, a statistical properties feature extra...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2014-05-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.05.003/ |
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| Summary: | Aiming at the application layer distributed deny of service(App-DDoS) attacks, a K-means multiple principal component analysis algorithm(KMPCAA) utilizing the Web log mining was proposed, then an App-DDoS detection method based on KMPCAA was presented. Firstly, a statistical properties feature extracting method was designed by ana-lyzing the difference between normal users' and attackers' access behavior. Secondly, a k-means multiple principal com-ponent analysis algorithm was proposed by using the maximum distance classification method according to the data di-mension reduction property of the principal component analysis, and then the testing model based on the algorithm was established. Finally, an App-DDoS attack detection experiment on the CTI-DATA dataset and the simulated attack data-set was conducted. In this experiment, the proposed method was compared with the fuzzy synthetical evaluation (FSE) algorithm, the hidden semi-Markov model (HsMM) detection algorithm and the dempster-shafer evidence theory (D-S) algorithm. Experimental results demonstrate that the KMPCAA detection algorithm has better detection performance. |
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| ISSN: | 1000-436X |