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Issue No. 03 - May-June (2017 vol. 14)
ISSN: 1545-5971
pp: 279-293
Vittorio P. Illiano , Department of Computing, Imperial College London, London, United Kingdom
Luis Munoz-Gonzalez , Department of Computing, Imperial College London, London, United Kingdom
Emil C. Lupu , Department of Computing, Imperial College London, London, United Kingdom
ABSTRACT
Wireless Sensor Networks carry a high risk of being compromised, as their deployments are often unattended, physically accessible and the wireless medium is difficult to secure. Malicious data injections take place when the sensed measurements are maliciously altered to trigger wrong and potentially dangerous responses. When many sensors are compromised, they can collude with each other to alter the measurements making such changes difficult to detect. Distinguishing between genuine and malicious measurements is even more difficult when significant variations may be introduced because of events, especially if more events occur simultaneously. We propose a novel methodology based on wavelet transform to detect malicious data injections, to characterise the responsible sensors, and to distinguish malicious interference from faulty behaviours. The results, both with simulated and real measurements, show that our approach is able to counteract sophisticated attacks, achieving a significant improvement over state-of-the-art approaches.
INDEX TERMS
Correlation, Wireless sensor networks, Temperature measurement, Pollution measurement, Monitoring, Transforms, Temperature sensors
CITATION

V. P. Illiano, L. Munoz-Gonzalez and E. C. Lupu, "Don't fool Me!: Detection, Characterisation and Diagnosis of Spoofed and Masked Events in Wireless Sensor Networks," in IEEE Transactions on Dependable and Secure Computing, vol. 14, no. 3, pp. 279-293, 2017.
doi:10.1109/TDSC.2016.2614505
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