AI-Driven Anomaly Detection in IoT Systems
Cong Doan Truong, Quang Thanh Duong, Thi Hong Hanh Nguyen, Van Dai Pham · 2025
The rapid growing Internet of Things applications has transformed industries and daily life, enabling real-time data collection and processing. However, this connectivity also introduces security vulnerabilities, particularly in anomaly detection. This paper explores AI-driven techniques for detecting anomalies in IoT systems, emphasizing their practical applications. The findings highlight the robust detection methods to secure networks and enhance reliability. By reviewing current AI-powered approaches, this research aims to inform and inspire future advancements in the field.