Using AI for IoT Device Anomaly Detection in Edge-Resident Intrusion Detection Systems (IDoS)

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This study offers a cutting-edge method for enhancing the security of Internet of Things (IoT) devices by using an Edge-Resident Intrusion Detection System (IDoS) driven by artificial intelligence (AI). By using edge computing to move anomaly detection capabilities closer to the source of IoT data, the proposed method solves latency, bandwidth, and scalability constraints. Through the application of advanced machine learning techniques, the Edge-Resident IDoS compares typical and aberrant patterns in real-time device behavior. This technology may be immediately installed on edge devices or local servers, which maximizes bandwidth and reduces dependency on centralized cloud solutions. It lowers latency as well. AI-driven anomaly detection ensures a proactive approach to security by identifying potential threats before they become more serious. The practical implications, which include impacts on compliance, energy efficiency, and dependability, show the system's usefulness in supporting safe and successful IoT deployments. The Edge-Resident IoT ecosystem is a ground-breaking step towards securing connected environments via the convergence of edge computing and AI. It supports resilient and adaptable IoT ecosystems.