Hybrid deep-learning analysis for cyber anomaly detection
Stanimir Ivanov Kabaivanov, Veneta Markovska · IOP Conference Series Materials Science and Engineering · 2020
Abstract Cyber threats evolve continuously and so do the detection tools and algorithms. In this paper we analyse the efficiency of hybrid deep-learning analysis as a mean to detect anomalies in computer network traffic. Different deep-learning algorithms are tested against real network intrusion events in an attempt to assess their potential as an early warning system. We suggest a combination of algorithms and rule-based filters as a hybrid system that can improve efficiency and accuracy of cyber anomaly detection.