Junyuan Shen^*, Jidong Wang, Hao Ai

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RMIT University, Melbourne, Australia.
Nanjing University of Posts and Telecommunications, Nanjing, China.
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Abstract: With theincreasing worldwide network attacks, intrusion detection (ID) hasbecome a popularresearch topic inlast decade.Several artificial intelligence techniques such as neural networks and fuzzy logichave been applied in ID. The results 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.

Keywords: Intrusion Detection; Negative Selection; Artificial Immune System; KDD CUP 99

Cite this paper: J. Shen, J. Wang and H. Ai, "An Improved Artificial Immune System-Based Network Intrusion Detection by Using Rough Set," Communications and Network, Vol. 4 No. 1, 2012, pp. 41-47. doi: 10.4236/cn.2012.41006.

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