Zihao Shen1,2, Hui Wang1,*, Kun Liu1, Peiqian Liu1, Menglong Ba1, MengYao Zhao3
Computer Systems Science and Engineering, Vol.37, No.1, pp. 121-133, 2021, DOI:10.32604/csse.2021.014988
- 05 February 2021
Abstract The rapid progress of the Internet has exposed networks to an increased number of threats. Intrusion detection technology can effectively protect network security against malicious attacks. In this paper, we propose a ReliefF-P-Naive Bayes and softmax regression (RP-NBSR) model based on machine learning for network attack detection to improve the false detection rate and F1 score of unknown intrusion behavior. In the proposed model, the Pearson correlation coefficient is introduced to compensate for deficiencies in correlation analysis between features by the ReliefF feature selection algorithm, and a ReliefF-Pearson correlation coefficient (ReliefF-P) algorithm is proposed. Then, More >
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