Adversarial Analysis of ML-Based Anomaly Detection in Multi-Layer Network Automation

The fast development of multi-layer packet-over-optical networks has made network monitoring and troubleshooting increasingly complicated. This has stimulated people to combine machine learning (ML) and software-defined networking (SDN) to realize multi-layer network automation. Despite its initial successes, the vulnerabilities of multi-layer network automation have not been fully explored. This work studies how to mislead the ML-based classifiers for anomaly detection. Specifically, we design two adversarial-sample-based attack schemes based on the white-box attack (WBA) and black-box attack (BBA) strategies, respectively, to eavesdrop and tamper legitimate telemetry data samples and generate adversarial samples adaptively, for disturbing ML-based classifiers and in turn misleading network automation to make incorrect decisions. Compared with WBA, BBA makes the attack scheme more practical by minimizing the dependency on pre-knowledge of the target ML-based classifiers. Considering different types of ML-based classifiers, we build a real-world packet-over-optical testbed and leverage the telemetry samples collected in it to demonstrate that our proposed BBA scheme can interact with the network quietly to train itself, generate well-crafted adversarial samples to tamper legitimate telemetry samples in the hard-to-detect way, and mislead ML-based classifiers in the network automation system to severely affect their performance on anomaly detection.