Anomaly Detection Using ML Techniques in Wireless Sensor Network

Abstract

Wireless Sensor Networks (WSNs) are essential in modern applications such as environmental monitoring, healthcare, and smart cities. However, these networks are vulnerable to anomalies caused by faults, cyber-attacks, and environmental factors, which can severely affect their performance and reliability. This paper presents an advanced framework for anomaly detection that employs machine learning techniques to improve the security and reliability of WSNs. By examining patterns in sensor data, the proposed method effectively identifies deviations that suggest potential anomalies. It combines both supervised and unsupervised learning algorithms to detect both known and unknown anomalies with high precision. The framework is designed to minimize false positives, improve detection precision, and ensure the robustness of WSN operations. Extensive experiments were carried out to assess the system’s performance, showing its capability to achieve high detection accuracy, minimize false alarms, and adapt to dynamic network conditions. The results highlight the potential of machine learning in enhancing WSN security, paving the way for the creation of more resilient and secure network infrastructures.