Research Article
Intrusion Detection System Based on Improved Artificial Immune Algorithm
@INPROCEEDINGS{10.1007/978-3-030-89814-4_41, author={Jilin Wang and Zhongdong Wu and Guohua Wang}, title={Intrusion Detection System Based on Improved Artificial Immune Algorithm}, proceedings={Mobile Multimedia Communications. 14th EAI International Conference, Mobimedia 2021, Virtual Event, July 23-25, 2021, Proceedings}, proceedings_a={MOBIMEDIA}, year={2021}, month={11}, keywords={Intrusion detection Artificial immune algorithm Negative selection algorithm}, doi={10.1007/978-3-030-89814-4_41} }- Jilin Wang
Zhongdong Wu
Guohua Wang
Year: 2021
Intrusion Detection System Based on Improved Artificial Immune Algorithm
MOBIMEDIA
Springer
DOI: 10.1007/978-3-030-89814-4_41
Abstract
Artificial immunity is widely used in the field of intrusion detection by simulating the accurate identification function of biological immune system to foreign intrusions, among which negative selection algorithm is the most widely used. However, due to the large amount of network data and high dimensionality, it often leads to problems such as low detection accuracy. In this paper, the method of combining principal component analysis (PCA) with genetic algorithm (GA) and negative selection algorithm improves the accuracy of intrusion detection. Among them, principal component analysis performs dimensionality reduction and feature extraction on intrusion data, and genetic algorithm is used to optimize the generation part of detector. The performance test was performed on the NSL-KDD standard test data set. The results show that this method improves the accuracy of intrusion detection and reduces the false alarm rate, which proves the effectiveness of the method.
