Trust Management in WSN using ML for Detection of DDoS Attacks

doi:10.23940/ijpe.25.03.p5.157167

Abstract

Abstract: Wireless Sensor Networks (WSNs) have emerged as an attractive solution for many challenging applications, including but not limited to environmental monitoring, health care, or industrial automation. Introduction to Trust Management in WSNs using ML(TMWSNML) is a significant part of WSN security, especially DDoS attacks. However, these networks also face many security threats such as DDoS (Distributed Denial of Service) attacks being one of the major challenges. To overcome such threats, the proposed model uses five machine learning models (Random Forest (RF), K-Nearest Neighbors (KNN), Decision Trees (DT), Support Vector Machines (SVM), and XGBoost). Experimental Findings of TMWSNML Algorithm achieves better metrics than existing lightweight methods in terms of Detection Rate (Accuracy), Precision, Recall, and F1-Score. In particular, RF achieves a stunning performance of 99.81% in all measurements, but KNN and DT also improve significantly with 99.55% and 99.74%, respectively. This indicates the favorability of SVM, while a similar result was also achieved by XGBoost which gave 99.3% accuracy while a 99.8% detection rate. The results demonstrate the capability of TMWSNML in the timely detection of DDoS attacks, therefore maintaining strong trust management and improving the security of WSN. This study highlights the power of machine learning in strengthening WSNs against emerging cyber threats.

Key words: DDoS attacks, machine learning, trust management, wireless sensor network

Vikas, Charu Wahi, Bharat Bhushan Sagar, and Manisha Manjul. Trust Management in WSN using ML for Detection of DDoS Attacks [J]. Int J Performability Eng, 2025, 21(3): 157-167.

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