Intrusion Detection Systems in Cloud Computing Paradigm: Analysis and Overview

• Published in: Complexity (New York, N.Y.) Vol. 2022; no. 1
• Main Authors: Rana, Pooja, Batra, Isha, Malik, Arun, Imoize, Agbotiname Lucky, Kim, Yongsung, Pani, Subhendu Kumar, Goyal, Nitin, Kumar, Arun, Rho, Seungmin
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2022; Hindawi Limited; Hindawi-Wiley
• Subjects: Accuracy; Applications programs; Cloud computing; Comparative analysis; Cybercrime; Cybersecurity; Datasets; Genetic algorithms; Intrusion detection systems; Literature reviews; Machine learning; Malware; Mobile computing; Optimization; Support vector machines
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2022/3999039

Cloud computing paradigm is growing rapidly, and it allows users to get services via the Internet as pay-per-use and it is convenient for developing, deploying, and accessing mobile applications. Currently, security is a requisite concern owning to the open and distributed nature of the cloud. Copious amounts of data are responsible for alluring hackers. Thus, developing efficacious IDS is an imperative task. This article analyzed four intrusion detection systems for the detection of attacks. Two standard benchmark datasets, namely, NSL-KDD and UNSW-NB15, were used for the simulations. Additionally, this study highlights the proliferating challenges for the security of sensitive user data and gives useful recommendations to address the identified issues. Finally, the projected results show that the hybridization method with support vector machine classifier outperforms the existing techniques in the case of the datasets investigated.