A SURVEY OF AI-DRIVEN INTRUSION DETECTION SYSTEMS FOR CLOUD AND EDGE COMPUTING: TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS

Abstract: The research presents a comprehensive survey of AI-driven intrusion detection systems designed for cloud and edge computing environments. The review systematically analyses recent research developments between 2015 and 2025, focusing on the application of supervised, unsupervised, semi-supervised, and hybrid learning techniques for network intrusion detection. It examines widely used algorithms such as Support Vector Machines, Random Forests, Convolutional Neural Networks, Recurrent Neural Networks, and emerging models including Transformer architectures and Graph Neural Networks. In addition, the survey evaluates commonly used benchmark datasets, such as NSL-KDD, CIC-IDS2017, and UNSW-NB15, which are widely employed to assess detection performance and model generalization. AI-driven intrusion detection systems represent a promising direction for strengthening cybersecurity in distributed cloud and edge computing ecosystems. By integrating advanced machine learning techniques with scalable and privacy-aware architectures, future IDS solutions can provide more intelligent, resilient, and proactive defence mechanisms against increasingly sophisticated cyber threats.

Keywords: Application, Algorithms, Models, Datasets.