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Investigation of computational intelligence techniques for intrusion detection in wireless sensor networks.

McDermott, Christopher D.; Petrovski, Andrei

Authors

Christopher D. McDermott



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.

Citation

MCDERMOTT, C.D. and PETROVSKI, A. 2017. Investigation of computational intelligence techniques for intrusion detection in wireless sensor networks. International journal of computer networks and communications [online], 9(4), pages 45-56. Available from: https://doi.org/10.5121/ijcnc.2017.9404

Journal Article Type Article
Acceptance Date Jul 31, 2017
Online Publication Date Jul 31, 2017
Publication Date Aug 31, 2017
Deposit Date Oct 5, 2017
Publicly Available Date Oct 5, 2017
Journal International journal of computer networks and communications
Print ISSN 0975-2293
Electronic ISSN 0974-9322
Publisher AIRCC Publishing Corporation
Peer Reviewed Peer Reviewed
Volume 9
Issue 4
Pages 45-56
DOI https://doi.org/10.5121/ijcnc.2017.9404
Keywords Wireless Sensor Networks (WSNs); Intrusion Detection System (IDS); Denial of service (DoS); Artificial Neural Network (ANN); Feedforward backpropagation; Support Vector Machine (SVM)
Public URL http://hdl.handle.net/10059/2526

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