A Modified Gray Wolf Optimizer-Based Negative Selection Algorithm for Network Anomaly Detection

Abstract

Intrusion detection systems are crucial in fighting against various network attacks. By monitoring the network behavior in real time, possible attack attempts can be detected and acted upon. However, with the development of openness and flexibility of networks, artificial immunity-based network anomaly detection methods lack continuous adaptability and hence have poor detection performance. Thus, a novel framework for network anomaly detection with adaptive regulation is built in this paper. First, a heuristic dimensionality reduction algorithm based on unsupervised clustering is proposed. This algorithm uses the correlation between features to select the best subset. Then, a hybrid partitioning strategy is introduced in the negative selection algorithm (NSA), which divides the feature space into a grid based on the sample distribution density and generates specific candidate detectors in the boundary grid to effectively mitigate the holes caused by boundary diversity. Finally, the NSA is improved by self-set clustering and a novel gray wolf optimizer to achieve adaptive adjustment of the detector radius and position. The results show that the proposed NSA algorithm based on mixed hierarchical division and gray wolf optimization (MDGWO-NSA) achieves a higher detection rate, lower false alarm rate, and better generation quality than other network anomaly detection algorithms.

Application of Deep Neural Network with Frequency Domain Filtering in the Field of Intrusion Detection

“…Balasubramaniam et al [43] proposed the Gradient Hybrid Leader Optimization (GHLBO) algorithm to train Deep Stacked Autoencoders (DSAs) for efective DDoS attack detection. Yang et al [44] introduced a hybrid partitioning strategy in the Negative Selection Algorithms (NSAs), which divides the feature space into grids based on the density of sample distributions. Tis strategy generates specifc candidate detectors in the boundary grids to efectively mitigate vulnerabilities caused by boundary diversity.…”

Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection

“…In 2014, the Grey Wolf Optimizer (GWO) was proposed 25 , a population-based metaheuristic algorithm that mimics the social hierarchy and group hunting behavior of grey wolves. Owing to its inherent simplicity, fewer requirements for control parameters, and strong optimization performance, the GWO has found extensive applications across engineering problems ?, 26 , anomaly detection 27 , band selection 28 , path planning 29,30 , FS [31][32][33] , and other fields [34][35][36] . Wang et al 37 developed a role-oriented binary GWO.…”