A Review of Intrusion Detection Systems Using Machine Learning: Attacks, Algorithms and Challenges

Cybersecurity has become a priority concern of the digital society. Many attacks are becoming more sophisticated, requiring strengthening the strategies of identification, analysis, and management of vulnerability to stop threats. Intrusion Detection/Prevention Systems are first security devices to protect systems. This paper presents a survey of several aspects to consider in machine learning-based intrusion detection systems. This survey presents the Intrusion Detection Systems taxonomy, the types of attacks they face, as well as the organizational infrastructure to respond to security incidents. The survey also describes several investigations to detect intrusions using Machine Learning, describing in detail the databases used. Moreover, the most accepted metrics to measure the performance of algorithms are presented. Finally, the challenges are discussed motivating future research.