Research Article
Network intrusion detection based on Chaotic Multi-Verse Optimizer
@INPROCEEDINGS{10.4108/eai.21-6-2018.2276603, author={Guozhu Liu and Bolun Zhang and Xinglu Ma and Jingjing Wang}, title={Network intrusion detection based on Chaotic Multi-Verse Optimizer}, proceedings={11th EAI International Conference on Mobile Multimedia Communications}, publisher={EAI}, proceedings_a={MOBIMEDIA}, year={2018}, month={9}, keywords={multi-verse optimizer; chaotic maps; intrusion detection; svm}, doi={10.4108/eai.21-6-2018.2276603} }- Guozhu Liu
Bolun Zhang
Xinglu Ma
Jingjing Wang
Year: 2018
Network intrusion detection based on Chaotic Multi-Verse Optimizer
MOBIMEDIA
EAI
DOI: 10.4108/eai.21-6-2018.2276603
Abstract
Network data with large amount and high dimensional features usually leads to a not high accuracy of detection when the support vector machine (SVM) is used in the intrusion detection. Principal component analysis (PCA) combined with CMVO-SVM is adopted to improve the accuracy of intrusion detection. Among them, PCA is used for dimensionality reduction and feature extraction on intrusion data and the parameter selection of SVM is optimized by Chaotic Multi-Verse optimizer (CMVO). Performance is tested on KDDCUP99 standard test data set. By comparing the results,the effectiveness of the method is showed through the increased accuracy of intrusion detection,decreased false alarm rate and false negative rate.
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