Malicious Node Detection in a Wireless Sensor Network

Abstract

Wireless Sensor Networks (WSNs) are vulnerable to malicious attacks that can disrupt operations and compromise data integrity. This paper proposes a method for detecting malicious node in WSNs using the Cooja network simulator and machine learning (ML) algorithms, specifically Random Forest and SVM. The approach involves collecting a dataset of normal and malicious traffic patterns simulated in Cooja. Features are extracted from the network traffic data, including packet size, addresses, timing, frequency, and network topology. Feature selection techniques identify informative features for distinguishing between normal and malicious node. The dataset splits into testing and training sets, and the Random Forest algorithm is trained using the training set. Performance evaluation measures accuracy, precision, recall, and F1-score. Additionally to enhance detection performance further, the SVM algorithm is incorporated. Known for its ability to handle high-dimensional data and separate complex decision boundaries, SVM constructs a hyperplane for effective identification of malicious nodes in the WSN.. The optimized models are deployed in real-time WSN environments to monitor incoming traffic continuously. Alerts are generated upon detecting malicious node, enabling prompt response and mitigation. This proposed method offers an effective means of detecting malicious node, improving the security and reliability of WSNs. The results highlight the potential of machine learning algorithms, specifically SVM and Random Forest, in accurately classifying and identifying malicious patterns. By incorporating these techniques, robust security mechanisms for WSNs can be developed.