Deep Generative Model with Isolation Forest (DGM-IF) for Unsupervised Anomaly Detection in Wireless Sensor Network and Internet of Things
Meriem Zerkouk, Miloud Mihoubi, Belkacem Chikhaoui · 2023
Anomaly detection is crucial for maintaining the reliability and security of wireless sensor networks and loT systems. Conventional methods require labeled data, often unavailable in these systems, making unsupervised techniques essential. Deep learning has shown promise in unsupervised anomaly detection tasks, including wireless sensor networks and loT applications. The Isolation Forest, an unsupervised anomaly detection algorithm, isolates anomalies based on in-herent properties. Combining deep learning with the Isolation Forest can improve accuracy and effectiveness in detecting anomalies. This paper presents the Deep Generative Model with Isolation Forest (DGM-IF), a novel unsupervised anomaly detection method for wireless sensor networks and loT. DGM-IF integrates deep generative models with the Isolation Forest algorithm to learn a robust representation of normal data and identify anomalies. The model generates synthetic data based on the learned distribution and employs the Isolation Forest to separate deviating data points. The proposed technique is assessed using real-world datasets and benchmarked against cutting-edge methods, proving its efficacy in detecting anomalies. The DGM-IF approach has the potential to significantly enhance the reliability and security of wireless sensor networks and IoT systems by identifying potential threats and attacks.