Network intrusion monitoring based on improved ant colony algorithm

RuoYuan Zhang; XingHang Wang

doi:10.1117/12.2640698

3 October 2022 Network intrusion monitoring based on improved ant colony algorithm

RuoYuan Zhang, XingHang Wang

Proceedings Volume 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022); 122900D (2022) https://doi.org/10.1117/12.2640698
Event: International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 2022, Zhuhai, China

Abstract

To solve the problems of low detection accuracy, long modeling time and slow convergence in intrusion detection system (IDS), an intrusion detection system feature selection method based on improved ant colony algorithm was proposed. In this method, an improved ant colony algorithm is used to optimize irrelevant features in data, and the optimal subset of features is selected by considering three indicators: true positive rate (TPR), false positive rate (FPR) and number of features. Experimental results show that compared with the existing feature selection algorithms, the proposed algorithm has more advantages in reducing the number of features required for robust IDS construction on the premise of ensuring high detection rate and low false positive rate.

Citation Download Citation

RuoYuan Zhang and XingHang Wang "Network intrusion monitoring based on improved ant colony algorithm", Proc. SPIE 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 122900D (3 October 2022); https://doi.org/10.1117/12.2640698

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KEYWORDS

Feature selection

Feature extraction

Detection and tracking algorithms

Binary data

Computer intrusion detection

Optimization (mathematics)

Data modeling

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