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G. Doziert, D. Brownf, J. Hurley and K. Cainf, “Vulnerability Analysis of AIS-Based Intrusion Detection Systems via Genetic and Particle Swarm Red Teams,” Evolutionary Computation, Vol. 1, 2004, pp. 111-116.

has been cited by the following article:

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

  AUTHORS: Junyuan Shen, Jidong Wang, Hao Ai

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

  JOURNAL NAME: Communications and Network, Vol.4 No.1, February 27, 2012

  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.