PORTFILER: Port-Level Network Profiling for Self-Propagating Malware Detection
This addresses the challenge of early SPM detection for network security operations, though it appears incremental as it builds on existing anomaly detection techniques with a novel ensemble method.
The authors tackled the problem of detecting self-propagating malware (SPM) attacks early by proposing PORTFILER, a machine learning system that uses port-level network traffic profiling. It achieved precision over 0.94 with low false positive rates in top-ranked alerts and outperformed deep-learning-based methods in detecting attacks like WannaCry and Mirai.
Recent self-propagating malware (SPM) campaigns compromised hundred of thousands of victim machines on the Internet. It is challenging to detect these attacks in their early stages, as adversaries utilize common network services, use novel techniques, and can evade existing detection mechanisms. We propose PORTFILER (PORT-Level Network Traffic ProFILER), a new machine learning system applied to network traffic for detecting SPM attacks. PORTFILER extracts port-level features from the Zeek connection logs collected at a border of a monitored network, applies anomaly detection techniques to identify suspicious events, and ranks the alerts across ports for investigation by the Security Operations Center (SOC). We propose a novel ensemble methodology for aggregating individual models in PORTFILER that increases resilience against several evasion strategies compared to standard ML baselines. We extensively evaluate PORTFILER on traffic collected from two university networks, and show that it can detect SPM attacks with different patterns, such as WannaCry and Mirai, and performs well under evasion. Ranking across ports achieves precision over 0.94 with low false positive rates in the top ranked alerts. When deployed on the university networks, PORTFILER detected anomalous SPM-like activity on one of the campus networks, confirmed by the university SOC as malicious. PORTFILER also detected a Mirai attack recreated on the two university networks with higher precision and recall than deep-learning-based autoencoder methods.