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Constant evolution and the emergence of new cyberattacks require thedevelopment of advanced techniques for defense. This paper aims to measure theimpact of a supervised filter (classifier) in network anomaly detection. Weperform our experiments by employing a hybrid anomaly detection approach innetwork flow data. For this purpose, we extended a state-of-the-artautoencoder-based anomaly detection method by prepending a binary classifieracting as a prefilter for the anomaly detector. The method was evaluated on thepublicly available real-world dataset UGR'16. Our empirical results indicatethat the hybrid approach does offer a higher detection rate of known attacksthan a standalone anomaly detector while still retaining the ability to detectzero-day attacks. Employing a supervised binary prefilter has increased the AUCmetric by over 11%, detecting 30% more attacks while keeping the number offalse positives approximately the same.

Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection