As computer networks keep growing at a high rate, achieving confidentiality, integrity, and availability of the information system is essential. Intrusion detection systems (IDSs) have been widely used to monitor and secure networks. The two major limitations facing existing intrusion detection systems are high rates of false-positive alerts and low detection rates on zero-day attacks. To overcome these problems, we need intrusion detection techniques that can learn and effectively detect intrusions. Hybrid methods based on machine learning techniques have been proposed by different researchers. These methods take advantage of the single detection methods and leverage their weakness. Therefore, this paper reviews 111 related studies in the period between 2012 and 2022 focusing on hybrid detection systems. The review points out the existing gaps in the development of hybrid intrusion detection systems and the need for further research in this area.
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