|
 |
|
|
|
An application to estimate the cyber-risk detection skill of mobile device users.
G. Schaff, C. Harpes, R. Martin, and M. Junger,
October 27 - November 1, 2013 - Venice, Italy.
The Sixth International Conference on Advances in Human oriented and Personalized Mechanisms, Technologies, and Services; IARIA conference; ISBN: 978-1-61208-306-3.
Location Assurance and Privacy in GNSS Navigation.
X. Chen, C. Harpes, G. Lenzini, S. Mauw, and J. Pang,
July 2013.
Demonstrating a trust framework for evaluating GNSS signals integrity.
X. Chen, C. Harpes, G. Lenzini, M. Martins, S. Mauw, and J. Pang,
June 2013, In Proc. 20th ACM Conference on Computer and Communication Security (CCS), pages 1329-1332. ACM Press.
A trust framework for evaluating GNSS signals integrity.
X. Chen, G. Lenzini, M. Martins, S. Mauw, and J. Pang,
June 2013, In Proc. 26th IEEE Security Fondations Symposium (CSF), pages 179-192. IEEE Computer Society.
Detecting Meaconing Attacks by Analysing the Clock Bias of Gnss Receivers.
D. Marnach, S. Mauw, M. Martins, and C. Harpes,
January 2013, Volume 48, Issue 2, Pages 63–83, ISSN (Online) 2083-6104, ISSN (Print) 0208-841X, DOI: 10.2478/arsa-2013-0006.
Existing Global Navigation Satellite Systems offer no authentication of their satellite signals towards their civilian users. As a consequence, several types of GNSS-related attacks, including meaconing, may be performed and remain undetected. In the scope of the project “Developing a prototype of Localisation Assurance Service Provider”, which is funded by ESA and realised by the company itrust consulting and the University of Luxembourg, a methodology to visualise the beginnings and the ends of meaconing attacks by monitoring the clock bias of an attacked receiver over time was developed. This paper presents an algorithm that is based on this attack visualisation technique and is capable of detecting meaconing attacks automatically. Experiments in a controlled environment confirmed that the chosen methodology works properly. In one of these tests, for example, six meaconing attacks were simulated by using a GNSS signal repeater. The algorithm was able to detect the beginnings and the ends of all six attacks, while resulting in no more than two false positives, even though the average delay introduced by the meaconing stations (repeater) was just 80 nanoseconds.