Research and Applications on Detector Generation Algorithm Based on Neural Networks

doi:10.3969/j.issn.1671-1122.2015.09.055

Abstract

Abstract:

Negative selection algorithm (NSA) is an important method of generating artiﬁcial immune detectors, and efficient detector generation algorithm is the kernel of intrusion detection. Aiming at conventional NSA detectors are not adaptive for dealing with time-varying circumstances, this paper analyzed the negative selection algorithm principle in an artificial immune system, and put forward a detector generation algorithm based on neural networks. Taking advantage of efficient neural networks training, it has the distinguishing capability of adaptation. Experimental results show that the algorithm performs well that it improves the detection rate and reduces the false dtection rate.

Key words: artificial immune systems, negative selection, neural networks, detector generation algorithm

CLC Number:

TP309

Hai-bo WU. Research and Applications on Detector Generation Algorithm Based on Neural Networks[J]. Netinfo Security, 2015, 15(9): 249-252.

Figures/Tables 2

References 20

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