Anomaly Detection in Communication Systems Using Hypergraph Neural Networks Optimized via Self-Competition Particle Swarm Algorithm

The creation of a powerful intrusion detection system (IDS) is important due to the security implications of 6G wireless communications and ultra-densified networks that exceed the capabilities of existing systems. IDSs are inadequate to prevent ongoing unidentified attacks on wireless networks. In this manuscript, anomaly detection in communication systems via self-competition swarm optimized hypergraph neural networks with a self-competition particle swarm optimization algorithm (CSHNN-SCPSOA-DA-CS) is proposed. At first, the input data are collected from the CIC-IDS2017 dataset. The input data are pre-processed using Bayesian boundary trend filtering (BBTF) to remove unwanted data. The pre-processed data are then fed to the weighted leader search algorithm (WLSA), which is employed to select optimal features. The selected features are then provided to anomaly detection using central-smoothing hypergraph neural networks (CSHNN) for classifying and detecting attacks such as cross-site scripting (XSS), brute force, SQL injection, port scan, web attacks, botnet, infiltration, and distributed denial-of-service (DDoS). Hence, the self-competition particle swarm optimization algorithm (SCPSOA) is used to optimize the CSHNN, which accurately classifies anomaly detection. The proposed CSHNN-SCPSOA-DA-CS approach achieves 19.51%, 22.95%, and 30.56% higher accuracy; 16.60% compared to existing techniques such as SVM-AD-CN, FL-AN-IDS, and CNN-DAA-NBAD, respectively.