A Review of Deep Learning IDS for DDoS Attacks in WLANs

An Intrusion detection system (IDS) is a critical security organ which serves as a gatekeeper against the ever-evolving cyber space attacks. Among the recent technological advancement, high speed communication networks and Artificial Intelligence are notably the key driving factors for the evolution of such IDS from the rule-based to classic machine learning and recently, the deep learning using either manual feature engineering or representation learning techniques. This paper is a methodological literature review of various generations of machine learning-based IDS to assess their effectiveness. The review result is a pretext and blueprint for a new method that attempts to combine representation and manual feature learning techniques to effectively detect Distributed Denial of Service attacks (DDoS) in Wireless Local Area Networks (WLANs).

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