A Meta-analytic Review of Intelligent Intrusion Detection Techniques in Cloud Computing Environment

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.

@article{Raj2021,
  title     = {A Meta-analytic Review of Intelligent Intrusion Detection Techniques in Cloud Computing Environment},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {10},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Meghana G Raj and Santosh Kumar Pani},
  doi       = {10.14569/IJACSA.2021.0121023},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121023}
}