Intrusion detection systems in the cloud computing: A comprehensive and deep literature review

Abstract

Abrupt development of resources and rising expenses of infrastructure are leading institutions to take on cloud computing. Albeit, the cloud environment is vulnerable to various sorts of attacks. So, recognizing malicious software is one of the principal challenges in cloud security governance. Intrusion detection system (IDS) has turned to the most generally utilized element of computer system security that asserts the cloud from diverse sorts of attacks and threats. As evident, no systematic literature review exists that focuses on cloud computing usage within IDS processes. The previous investigations had not considered the statistical analysis method. Hence, this paper examined the IDS mechanisms in cloud computing systematically. Twenty-two articles have been obtained using defined filters divided into four sections: hypervisor-based IDS, network-based IDS, machine learning-based IDS, and hybrid IDS. The comparison is performed depending on the outcomes illustrated in the investigations. It demonstrates that IDS precision, inclusiveness, overhead, and reaction time have been discussed in many studies. Simultaneously, less attention has been paid to cost-sensitivity, functioning, attack tolerance, and intrusion facing. This paper has made an excellent effort to organize literature drawn from multiple sources into a manuscript.