Abstract
Security and data privacy continue to be major considerations in the selection and study of cloud computing. Organizations are migrating more critical operations to the cloud, resulting in increase in the number of cloud vulnerability incidents. In recent years, there have been several technological advancements for accurate detection of attacks in the cloud. Intrusion Detection Systems (IDS) are used to detect malicious attacks and reinstate network security in the cloud environment. This paper presents a systematic literature review and a meta-analysis to shed light on intelligent approaches for IDS in cloud. This review focuses on three intelligent IDS approaches- Machine Learning Algorithms, Computational Intelligence Algorithms and Hybrid Meta-Heuristic Algorithms. A qualitative review synthesis was carried out on a total of 28 articles published between 2016 and 2021. This study concludes that IDS based on Hybrid Meta-Heuristic Algorithms have increased Accuracy, decreased False Positivity Rate and increased Detection Rate.
Highlights
Cloud Computing (CC) provides on-demand network access to a group of configurable computing assets like servers, services, applications, storage, and networks that could be rapidly released with lesser management endeavors or service provider interaction
Systems (HIDS) functions on data collected from a computer system, and permits analysis of activities of processes and users in the attack on a specific system. It visualizes the attempted attack’s outcome, access and observe data files directly and the process of the operating system [25]. It identifies the attacks which may not have been detected by Network-based Intrusion Detection System (NIDS), as it observes the events which are local to the computer system
This paper provides a review of existing IDS algorithms, developed for the CC environment, with the objective of comparing the performance of IDS approaches along selected parameters
Summary
Cloud Computing (CC) provides on-demand network access to a group of configurable computing assets like servers, services, applications, storage, and networks that could be rapidly released with lesser management endeavors or service provider interaction. It visualizes the attempted attack’s outcome, access and observe data files directly and the process of the operating system [25] It identifies the attacks which may not have been detected by Network-based Intrusion Detection System (NIDS), as it observes the events which are local to the computer system. A second method is Anomaly-based which attempts to detect attacks or intrusions using machine learning and statistics to create simulations, which are compared with the current anomalies that may been seen in the cloud environment [51]. A comparative review of performance of various IDS approaches, after classifying them into different approach types- Machine Learning Algorithms, Computational Intelligence Algorithms and Hybrid Meta-Heuristic Algorithms- along selected parameters is not available in the literature. The open issues, possible future directions, and limitations of the study have been elaborated
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