Unsupervised gan-based intrusion detection system using temporal convolutional networks, self-attention, and transformers

Abstract

There is provided a method for detecting cyber-attacks at edge servers. The method comprises receiving network traffic. The method comprises extracting and normalizing network flow features from the network traffic to produce a data pattern for evaluation. The method comprises computing an anomaly detection score for the data pattern for evaluation. The method comprises comparing the anomaly detection score with a threshold and determining if the network traffic corresponds to normal traffic or to an anomaly. The method comprises, upon determining that the network traffic corresponds to an anomaly, mitigating malicious network traffic by triggering a mitigation strategy and sending an anomaly message to an anomaly classifier. A method for classifying cyber-attacks at a cloud server is also provided.