An improved Artificial Immune Systembased Network Intrusion Detection by Using Rough Set
With theincreasing worldwide network attacks, intrusion detection (ID) hasbecome a popularresearch topic inlast dec- ade.Several artificial intelligence techniques such as neural networks and fuzzy logichave been applied in ID. The re- sults are varied. Theintrusion detection accuracy is themain foc...
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| Veröffentlicht in: | Tong xin yu wang lu 2012-02, Vol.4 (1), p.41-41 |
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| creator | Shen, Junyuan Wang, Jidong Ai, Hao |
| description | With theincreasing worldwide network attacks, intrusion detection (ID) hasbecome a popularresearch topic inlast dec- ade.Several artificial intelligence techniques such as neural networks and fuzzy logichave been applied in ID. The re- sults are varied. Theintrusion detection accuracy is themain focus for intrusion detection systems (IDS). Most research activities in the area aiming to improve the ID accuracy. In this paper, anartificial immune system (AIS) based network intrusion detection scheme is proposed. An optimized feature selection using Rough Set (RS) theory is defined. The complexity issue is addressed in the design of the algorithms. The scheme is tested on the widely used KDD CUP 99 dataset. The result shows that theproposed scheme outperforms other schemes in detection accuracy. |
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| subjects | Artificial intelligence Design engineering Expert systems Fuzzy logic Immune systems Intrusion Networks Neural networks |
| title | An improved Artificial Immune Systembased Network Intrusion Detection by Using Rough Set |
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