An Improved Artificial Immune System-Based Network Intrusion Detection by Using Rough Set

Junyuan Shen, Jidong Wang, Hao Ai

Nanjing University of Posts and Telecommunications, Nanjing, China.
RMIT University, Melbourne, Australia.

DOI: 10.4236/cn.2012.41006                

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

Conflicts of Interest

The authors declare no conflicts of interest.

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