Name
Abstract | ||
|---|---|---|
Based on the theory of local-world network, the composition self-similarity (CSS) of network traffic is presented for the first time in this paper for the study of DoS detection. We propose the concept of composition distribution graph and design the relative operations. The (R/S)(d) algorithm is designed for calculating the Hurst parameter. Based on composition distribution graph and Kullback Leibler (KL) divergence, we propose the composition self-similarity anomaly detection (CSSD) method for the detection of DoS attacks. We evaluate the effectiveness of the proposed method. Compared to other entropy based anomaly detection methods, our method is more accurate and with higher sensitivity in the detection of DoS attacks. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3837/tiis.2012.05.012 | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS |
Keywords | Field | DocType |
DoS detection,composition self-similarity,composition distribution graph,Kullback-Leibler divergence | Anomaly detection,Graph,D algorithm,Pattern recognition,Denial-of-service attack,Computer science,Hurst exponent,Artificial intelligence,Self-similarity,Kullback–Leibler divergence | Journal |
Volume | Issue | ISSN |
6 | 5 | 1976-7277 |
Citations | PageRank | References |
5 | 0.43 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianqi Zhu | 1 | 62 | 5.74 |
| Feng Fu | 2 | 8 | 0.80 |
| Chong-kwon Kim | 3 | 1038 | 102.46 |
| KeXin Yin | 4 | 10 | 2.55 |
| Yanheng Liu | 5 | 228 | 36.14 |