Malicious Node Detection in Wireless Sensor Networks Using Neural Networks
DOI:
https://doi.org/10.47839/ijc.24.2.4017Keywords:
Neural Networks (NN), Wireless Sensor Network WSN, malicious node, throughput, power consumption, delayAbstract
Throughout the past decade, wireless sensor networks (WSNs) have become a focus of observation in the wireless and mobile computing research community. The WSN has many uses, and applications from inside (starting from measuring temperature, pressure, humidity, and similar application inside the home) to outside (extension on the tactical battleground). Due to the distributed nature and spread in distant region, these networks are prone to many security attacks, which in turn negatively influences performances of these networks. Therefore, securing wireless sensor networks against these threats is becoming more and more important. A malicious node within the network is one of these threats. In this study, a procedure for detecting a malicious node in the network using the NS-2 simulator is presented. The proposed method uses Artificial Neural Network (ANN), built using MATLAB, for the purpose of prediction. Training, validation and testing have been performed on a dataset containing throughput, power consumption and time delay as inputs of the used ANN. Experimental results show the efficiency of the proposed method in security threats detection.
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