Paper Title
Real-time Intrusion Detection System for Distributed IOT Networks

Abstract
The rapid growth of IoT networks across industries has introduced significant security challenges due to their decentralized and resource-constrained nature. Traditional intrusion detection systems (IDS) fall short in addressing the dynamic and distributed architecture of IoT systems. This paper provides a comprehensive analysis of existing intrusion detection methodologies, focusing on approaches that integrate edge, fog, and cloud computing. By examining the strengths and limitations of various machine learning-enhanced and hybrid IDS frameworks, this review identifies critical gaps such as scalability issues, dataset dependency, and insufficient real-world validation. Building on these insights, we propose a novel real-time intrusion detection framework that leverages distributed processing across edge, fog, and cloud nodes to improve security, scalability, and energy efficiency in IoT networks. Keywords - Edge Computing, Fog Computing, Cloud Computing, IoT Security, Intrusion Detection System (IDS)