TY - JOUR AB - Wireless Sensor Networks carry a high risk of being compromised, as their deployments are often unattended, physicallyaccessible and the wireless medium is difficult to secure. Malicious data injections take place when the sensed measurements aremaliciously altered to trigger wrong and potentially dangerous responses. When many sensors are compromised, they can collude witheach other to alter the measurements making such changes difficult to detect. Distinguishing between genuine and maliciousmeasurements is even more difficult when significant variations may be introduced because of events, especially if more events occursimultaneously. We propose a novel methodology based on wavelet transform to detect malicious data injections, to characterise theresponsible sensors, and to distinguish malicious interference from faulty behaviours. The results, both with simulated and realmeasurements, show that our approach is able to counteract sophisticated attacks, achieving a significant improvement overstate-of-the-art approaches. AU - Illiano,V AU - Muñoz-Gonzàlez,L AU - Lupu,E DO - 10.1109/TDSC.2016.2614505 EP - 293 PY - 2016/// SN - 1545-5971 SP - 279 TI - Don't fool me!: Detection, Characterisation and Diagnosis of Spoofed and Masked Events in Wireless Sensor Networks T2 - IEEE Transactions on Dependable and Secure Computing UR - http://dx.doi.org/10.1109/TDSC.2016.2614505 UR - http://hdl.handle.net/10044/1/40329 VL - 14 ER -