Network intrusion monitoring based on improved ant colony algorithm
Ruoyuan Zhang, XingHang Wang · 2022
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.