Comprehensive Review of Intrusion Detection Systems in Cloud Computing
Keywords:
Intrusion Detection Systems, Cloud Security, Cyber security, Cloud Computing, Threat Detection, Anomaly Detection, Machine Learning, Security ChallengesAbstract
As network traffic volumes escalate and cyber threats grow increasingly sophisticated, there's an imperative to evolve intrusion detection systems (IDSes) with innovative approaches. Recent advancements in leveraging both machine learning (ML) and cloud computing technologies hold promise for bolstering threat identification capabilities and optimizing computation speed. This paper conducting a systematic literature review spanning the years 2016 to 2024 delves into the latest research at the nexus of ML and cloud computing, focusing on cloud-based network intrusion detection methodologies integrating ML algorithms (MLs). An ID plays a crucial role in identifying and mitigating security threats in cloud infrastructures. Leveraging ML algorithms offers promising avenues for enhancing IDS capabilities in such dynamic and complex environments. By spotlighting notable implementations from recent studies, we present a comprehensive overview of the evolving landscape of ML utilization in cloud-based network intrusion detection. This includes discussing achievements, hurdles, and future avenues for enhancing ML-driven approaches in this domain.
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