Research Spotlight: Learning Detectors of Malicious Network Traffic

By Talos Group This post was authored by Karel Bartos , Vojtech Franc , & Michal Sofka . Malware is constantly evolving and changing. One way to identify malware is by analyzing the communication that the malware performs on the network. Using machine learning, these traffic patterns can be utilized to identify malicious software. Machine learning faces two obstacles: obtaining a sufficient training set of malicious and normal traffic and retraining the system as malware evolves. This post will analyze an approach that overcomes these obstacles by developing a []

Source:: Cisco Security Notice