Don't fool me!: Detection, Characterisation and Diagnosis of Spoofed and Masked Events in Wireless Sensor Networks
File(s)
Published version
Accepted version
OA Location
Author(s)
Illiano, V
Muñoz-Gonzàlez, L
Lupu, E
Type
Journal Article
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.
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.
Date Issued
2016-10-26
Date Acceptance
2016-08-11
Citation
IEEE Transactions on Dependable and Secure Computing, 2016, 14 (3), pp.279-293
ISSN
1545-5971
Publisher
IEEE
Start Page
279
End Page
293
Journal / Book Title
IEEE Transactions on Dependable and Secure Computing
Volume
14
Issue
3
Copyright Statement
This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
License URL
Sponsor
Intel Corporation (UK) Ltd
Grant Number
PO #3000922346
Subjects
Science & Technology
Technology
Computer Science, Hardware & Architecture
Computer Science, Information Systems
Computer Science, Software Engineering
Computer Science
Information security
wireless sensor networks
event detection
continuous wavelet transforms
SECURITY
MODELS
0803 Computer Software
0804 Data Format
0805 Distributed Computing
Strategic, Defence & Security Studies
Publication Status
Published