Learning mechanisms for anomaly-based intruson detection: Updated review

With the proliferation of Internet connectivity to share information and provide online services, detecting malicious and misbehavior activities continues to be of major importance in cyber security. However, countering intrusive attacks is a challenging problem without a universal magic solution that can be successfully applied to all scenarios. A variety of machine learning and computational intelligence techniques have been extensively applied to detect these attacks. This paper reviews the state-of-the-art machine learning mechanisms for anomaly-based intrusion detection. It also covers several related datasets adopted to benchmark the proposed intrusion detection systems. Besides offering a critical up-to-date summary, it can serve as an instrumental pedagogical tool to help junior researchers conceive the vast amount of research work and gain a holistic view and awareness of various contemporary research directions in this vital domain.