Multi-scale convolutional auto encoder for anomaly detection in 6G environment

Abstract

With the increasing deployment of 6G networks in all industries, there is a growing risk of vulnerability and complexity due to continuous data flow from edge devices to specialized computers. One potential threat to 6G networks is fuzzing attacks, which involve sending random and invalid inputs to identify vulnerabilities and flaws. A successful fuzzing attack could compromise the network’s protocols, interfaces, and security mechanisms, leading to potential disruption or compromise of critical infrastructure. The proposed framework involves gathering relevant data from different sources within the 6G network and pre-processing it to prepare for analysis. Relevant features that indicate the presence of anomalies are identified using Tuna Swarm Optimization (TSO) Algorithm. The Multi-Scale Convolutional Auto Encoder (MSCAE) is then utilized by the proposed model to extract the feature and classify data. The intrusion Detection System is built to monitor and classify nodes producing anomalies. The proposed model is assessed utilizing various metrics, including recall, precision, accuracy, detection latency, and F1 score. The proposed framework achieved 97.50% accuracy, 94.81% precision, 93.50% F1-score, and 94.50% recall with notable improvements that surmount the deficiencies of previous studies. The results demonstrate that the proposed algorithm is more efficient and safe than current edge security methods, potentially mitigating the risk of fuzzing attacks in 6G networks.