AI-DRIVEN INTRUSION DETECTION SYSTEMS IN CLOUD ENVIRONMENTS

Abstract

The rapid adoption of cloud computing has introduced new complexities in cybersecurity, particularly in detecting and mitigating intrusions in dynamic, multi-tenant environments. Traditional intrusion detection systems (IDS) rely heavily on static rule-based approaches, which are often inadequate against the evolving landscape of cyber threats. This article explores the integration of Artificial Intelligence (AI) techniques in designing intelligent, adaptive, and autonomous intrusion detection systems for cloud environments. It examines various machine learning and deep learning models used for real-time anomaly detection, data preprocessing, and decision-making automation. The study also highlights challenges such as scalability, false alarm reduction, and privacy preservation in cloud-based IDS. The findings emphasize that AI-driven IDS not only enhance detection accuracy but also enable proactive threat mitigation through continuous learning and intelligent response mechanisms.