AI-Based Anomaly Detection for IoT Networks: A Comparative Analysis of Existing Techniques
Hamad Almansour · 2025
The expansion of Internet of Things (IoT) networks has revolutionized industries with the simplicity of interconnectivity and sharing of data. The expansion, nevertheless, has also escalated the vulnerability of IoT systems to cyber threats and anomalies that could affect functionality, compromise sensitive information, and endanger the safety of users. Anomaly detection has emerged as a critical defense mechanism to identify and avert possible threats from escalating. Artificial intelligence (AI) offers better capabilities in handling the complexity and amount of IoT data to enable more accurate and effective detection approaches than traditional techniques. The paper provides a comprehensive review and comparison of existing AI-driven techniques for anomaly detection in IoT networks. From an extensive survey of existing literature, the paper compares techniques such as traditional machine learning, deep learning, and hybrid approaches. The comparison is made on the basis of detection accuracy, scalability, computational overhead, and how easily the techniques translate to real-world IoT environments. Key observations are that while AI methods significantly enhance detection, problems such as high false-positive rates and computational overhead continue to be prevalent. The paper's contributions include a comparative study of the techniques, gaps present, and theoretical suggestions for enhancement. Consolidating the knowledge from current research, this study offers a practical guide for researchers and practitioners who aim to enhance IoT network security through AI-based anomaly detection.