MDiET: Malware Detection in Encrypted Traffic
Dimitrios Schoinianakis, Norbert Goetze, Gerald Lehmann · Electronic workshops in computing · 2019
With the increasing adoption of end-to-end encryption in industrial systems, the risk of distributing hidden malware by exploiting encrypted channels gradually turns to a major concern. Due to encryption, the state-of-the-art, signature-based mechanisms might fail to detect malware sufficiently, thus new approaches are required. In this work, a method for malware detection in encrypted traffic based on Machine Learning is presented. A supervised learning approach is adopted and the efficiency of the solution is demonstrated by a set of exhaustive simulations. Further considerations for incorporating the proposed method in a reference industrial network are also discussed.