Cyber Intrusion Detection Based on a Mutative Scale Chaotic Bat Algorithm with Backpropagation Neural Network - Xu, Xuebin; Qin, Hu; Zhou, Jie (Security and communication networks, 2022)
In the current complex cyber environment, malicious attacks take various forms and constantly change. To overcome the problems of low detection efficiency and high false alarm rate in traditional intrusion detection techniques, a new method of network intrusion detection combining the variable scale chaos bat (MSCB) algorithm and BP neural network (BPNN) called MSCB-BPNN is proposed in this paper. In the proposed approach, a new MSCB algorithm is proposed to improve the BPNN by optimizing its thresholds and weights so as to prevent it from falling into local optimum. To verify the practical classification performance of the proposed method for intrusion detection, some experiments are carried out. In these experiments, the proposed approach is compared with BPNN, SA-BPNN, and GA-BPNN on the benchmark intrusion detection datasets KDD cup 99 and UNSW-NB15. The experimental results show that the accuracy of the proposed method can reach 99.4% and 99.8% accuracy on the KDD cup 99 training and test sets, respectively, while 89.0% and 93.9% accuracy on the UNSW-NB15 training and test sets, respectively, which are higher than BPNN, SA-BPNN, and GA-BPNN. Furthermore, the precision and recall ratio of MSCB-BPNN are also superior to other approaches. Thus, the proposed model has significant advantages over BPNN, SA-BPNN, and GA-BPNN methods.