Investigation of Computational Intelligence Techniques for Intrusion Detection in Wireless Sensor Networks

Christopher D. McDermott and Andrei Petrovski 

School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, Scotland 

ABSTRACT 

Wireless Sensor Networks (WSNs) have become a key technology for the IoT and despite obvious benefits, challenges still exist regarding security. As more devices are connected to the internet, new cyber attacks are emerging which join well-known attacks posing significant threats to the confidentiality, integrity and availability of data in WSNs. In this work, we investigated two computational intelligence techniques for WSN intrusion detection. A back propagation neural network was compared with a support vector machine classifier. Using the NSL-KDD dataset, detection rates achieved by the two techniques for six cyber attacks were recorded. The results showed that both techniques offer a high true positive rate and a low false positive rate, making both of them good options for intrusion detection. In addition, we further show the support vector machine classifiers suitability for anomaly detection, by demonstrating its ability to handle low sample sizes, while maintaining an acceptable FPR rate under the required threshold.

KEYWORDS 

Wireless Sensor Networks (WSNs), Intrusion Detection System (IDS), Denial of Service (DoS), Artificial Neural Network (ANN), Feed-forward Backpropagation, Support Vector Machine (SVM).

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