Chaos Theory Based Detection against Network Mimicking DDoS Attacks
DDoS attack traffic is difficult to differentiate from legitimate network traffic during transit from the attacker, or zombies, to the victim. In this paper, we use the theory of network self-similarity to differentiate DDoS flooding attack traffic from legitimate self-similar traffic in the network...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
IEEE Communications Society
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/5327 |
| _version_ | 1848744766176493568 |
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| author | Chonka, A. Singh, Jaipal Zhou, W. |
| author_facet | Chonka, A. Singh, Jaipal Zhou, W. |
| author_sort | Chonka, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | DDoS attack traffic is difficult to differentiate from legitimate network traffic during transit from the attacker, or zombies, to the victim. In this paper, we use the theory of network self-similarity to differentiate DDoS flooding attack traffic from legitimate self-similar traffic in the network. We observed that DDoS traffic causes a strange attractor to develop in the pattern of network traffic. From this observation, we developed a neural network detector trained by our DDoS prediction algorithm. Our preliminary experiments and analysis indicate that our proposed chaotic model can accurately and effectively detect DDoS attack traffic. Our approach has the potential to not only detect attack traffic during transit, but to also filter it. |
| first_indexed | 2025-11-14T06:06:41Z |
| format | Journal Article |
| id | curtin-20.500.11937-5327 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:06:41Z |
| publishDate | 2009 |
| publisher | IEEE Communications Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-53272017-01-30T10:45:21Z Chaos Theory Based Detection against Network Mimicking DDoS Attacks Chonka, A. Singh, Jaipal Zhou, W. anomaly detection chaotic models Distributed denial-of-service (DDoS) DDoS attack traffic is difficult to differentiate from legitimate network traffic during transit from the attacker, or zombies, to the victim. In this paper, we use the theory of network self-similarity to differentiate DDoS flooding attack traffic from legitimate self-similar traffic in the network. We observed that DDoS traffic causes a strange attractor to develop in the pattern of network traffic. From this observation, we developed a neural network detector trained by our DDoS prediction algorithm. Our preliminary experiments and analysis indicate that our proposed chaotic model can accurately and effectively detect DDoS attack traffic. Our approach has the potential to not only detect attack traffic during transit, but to also filter it. 2009 Journal Article http://hdl.handle.net/20.500.11937/5327 IEEE Communications Society fulltext |
| spellingShingle | anomaly detection chaotic models Distributed denial-of-service (DDoS) Chonka, A. Singh, Jaipal Zhou, W. Chaos Theory Based Detection against Network Mimicking DDoS Attacks |
| title | Chaos Theory Based Detection against Network Mimicking DDoS Attacks |
| title_full | Chaos Theory Based Detection against Network Mimicking DDoS Attacks |
| title_fullStr | Chaos Theory Based Detection against Network Mimicking DDoS Attacks |
| title_full_unstemmed | Chaos Theory Based Detection against Network Mimicking DDoS Attacks |
| title_short | Chaos Theory Based Detection against Network Mimicking DDoS Attacks |
| title_sort | chaos theory based detection against network mimicking ddos attacks |
| topic | anomaly detection chaotic models Distributed denial-of-service (DDoS) |
| url | http://hdl.handle.net/20.500.11937/5327 |