AI-Driven Intrusion Detection Systems in Cloud Infrastructures: A Comprehensive Review of Hybrid Security Models and Future Directions

This review explores AI-driven Intrusion Detection Systems (IDS) in cloud infrastructures, comparing cutting-edge AI techniques with traditional security approaches. By analyzing machine learning (ML) and deep learning (DL) methodologies, the paper identifies both the advantages and limitations of each, advocating for hybrid models that combine conventional security protocols with AI-driven solutions. It addresses key challenges such as privacy compliance, scalability, and the reduction of false positives, offering practical strategies to overcome these issues. Additionally, the review highlights critical gaps in current research, including the need for more consistent, modern datasets and refined models. Emerging technologies, such as high-performance GPUs and quantum computing, are examined for their potential to enhance the complexity and efficiency of IDS in cloud environments. The paper concludes by outlining future research priorities, emphasizing the need for improved AI-driven anomaly detection, robust privacy safeguards, and adaptable security frameworks to meet evolving threats and regulatory demands in cloud security.