AI-ENHANCED INFORMATION SECURITY FRAMEWORKS FOR CLOUD-ENABLED IOT NETWORKS: A COMPREHENSIVE REVIEW
TL;DR
This review paper critically examines the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance information security within Cloud-IoT networks, focusing on hybrid Deep Learning models (CNN-LSTM), predictive analytics, and automated threat response mechanisms.
Abstract
The convergence of Cloud Computing and the Internet of Things (IoT) has created a hyper-connected ecosystem that drives modern industry but simultaneously expands the cyber-attack surface. Traditional security mechanisms, such as static firewalls and signature-based intrusion detection, are increasingly insufficient against sophisticated, polymorphic threats like Distributed Denial of Service (DDoS) and zero-day exploits. This review paper critically examines the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance information security within Cloud-IoT networks. We analyze the current state of AI-driven frameworks, focusing on hybrid Deep Learning models (CNN-LSTM), predictive analytics, and automated threat response mechanisms. A systematic literature survey of recent advancements identifies key trends, including the shift towards decentralized learning and real-time adaptive defense. Furthermore, we critically analyze the limitations of existing approaches, such as computational overhead and data privacy concerns. Finally, this paper identifies significant research gaps and provides strategic recommendations for developing robust, scalable, and intelligent security architectures for next-generation environments.