Learning mechanisms or anomaly-based intruson detection: Updated review

With the prolieraton of Internet connectivity o hare informaton and provide onlne services, detecting Mali cious and misbehavior activites contnues to be of major imporance in cyber security. However, countering intrusve atacks is a challenging problem wihout a universal magic soluton that can be succesully applied o all scenarios. A variety of machine earning and computatonal intellgence echniques have been extensvely appled o detect these atacks. This paper reviews the state-of the-art machine learning mechanisms for anomaly-based intruson detection. It also covers several related datasets adopted to benchmark the proposed intruson detection systems. Besdes ofering a critcal up-o-date summary, it can serve as an nstrumental pedagogical tool to help junior researchers conceive he vast amount of research work and gain a holic view and awareness of various contemporary research directions in his vital domain.