Securing and Optimizing Wireless Sensor Military Networks: A Hybrid KNN-Decision Tree Model for Anomaly Detection and False Alarm Reduction

Anushri Narendra Pathak^1,* , Arvind R. Yadav²

¹Research Scholar, E&C Dept, Parul Institute of Engineering and Technology, FET, Parul University, Vadodara, India

²Associate Professor, E&I Engineering Dept, Institute of Technology, Nirma University, Ahmedabad, India

Emails: anushrip21@gmail.com; arvind.yadav.me@gmail.com

Abstract

In applications related to military operations, Wireless Sensor Military Networks (WSMNs) aid a critical function by deploying a distributed group of sensor nodes. Such sensor networks lift the overall effectiveness of military activities by situational alertness and permitting instantaneous decision-making processes. This deployment also rises noteworthy challenges, namely scalability, energy efficiency, and security vulnerabilities. Ensuring the accessibility, trustfulness and confidentiality of the data sensed by sensor nodes is prime important challenge. It could lead to disastrous consequences on the military field. Looking into this shortfall, ongoing research is mainly targeted at obtaining advanced solutions to such challenges, such as secure and energy-efficient routing algorithms. However, one of the considerable challenges in WSNs is anomaly detection and the existence of false alarms. This can affect the dependability and effectiveness of the system. The ongoing research in this field focuses on exploring the condition of WSMN, mainly their applications, challenges, and future directions. Authors propose an adaptive and hybrid Machine Learning (ML) approach to reduce false alarms and anomaly detection along considering mutual authentication system. ML approaches offer reliable solutions by improving the data classification accuracy and detection of anomalies. These algorithms have better capability to distinguish between normal and abnormal events, which ultimately reduces false triggers. The authors propose a hybrid approach of k-Nearest Neighbors (KNN) and Decision Tree (DT), which results in a powerful method for improved classification accurateness and robustness in WSN. The effectiveness of KNN in local decision-making and better clear interpretability of Decision Tree to handle feature interactions are combined together in this strategy, to increase overall performance.

Keywords: Wireless Sensor Military Networks (WSMN); False Alarms; Energy-Aware; Machine learning; Decision-Tree; K-nearest neighbor