A Novel AI-Driven Approach for Real-Time Anomaly Detection in IoT Networks
Abstract
The rapid expansion of IoT networks has increased the demand for real-time anomaly detection to prevent security breaches and system failures. This paper introduces a novel AI-driven anomaly detection framework that combines deep learning with statistical anomaly detection techniques. We compare our approach against traditional rule-based and machine learning methods in smart city infrastructure, industrial automation, and network security applications. Experimental results reveal that our model achieves higher detection accuracy while minimizing false positives, making it an effective solution for IoT security.
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