A Comprehensive Review of AI-Powered Intrusion Detection Techniques in a Cloud Computing Environment

Abstract

In Cloud Computing, Artificial Intelligence (AI)-driven intrusion detection focuses on identifying anomalies, unauthorized access, and malicious activities across dynamic cloud environments. Besides, the Intrusion Detection System (IDS) is also crucial in strengthening the overall cybersecurity defenses, thereby assisting institutions or organizations in detecting malicious activities to improve their security and preserve sensitive data. Conventional security approaches often fail to handle constantly evolving attack patterns in the cloud. Prevailing signature-based schemes do not have the ability to identify unknown threats, thereby generating false alarms. Besides, they require large labelled databases for adapting to changing workloads. This survey examines several intrusion detection approaches in the Cloud Computing Environment. The methods are categorized as Machine Learning (ML), Federated Learning (FL), Deep Learning (DL), and Big Databased models. Further, to provide a comprehensive assessment, 25 research papers on intrusion detection are collected and reviewed. Further, a general outline for detecting intrusions is explained, and then the literature review of each technique with its pros and cons is elaborated. The research gaps that are encountered by the existing techniques are also presented. In addition, this survey also highlights the analysis on the basis of various factors, like publication year, methodology, tools, indicators, and databases utilized.