Inside Attack Filtering for Robust Sensor Localization
Title | Inside Attack Filtering for Robust Sensor Localization |
Publication Type | Conference Paper |
Year of Publication | 2016 |
Authors | Won, Jongho, Bertino, Elisa |
Conference Name | Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-4233-9 |
Keywords | Human Behavior, localization, pubcrawl, Resiliency, Scalability, security, sensor security, Wireless sensor networks |
Abstract | Several solutions have recently been proposed to securely estimate sensor positions even when there is malicious location information which distorts the estimate. Some of those solutions are based on the Minimum Mean Square Estimation (MMSE) methods which efficiently estimate sensor positions. Although such solutions can filter out most of malicious information, if an attacker knows the position of a target sensor, the attacker can significantly alter the position information. In this paper, we introduce such a new attack, called Inside-Attack, and a technique that is able to detect and filter out malicious location information. Based on this technique, we propose an algorithm to effectively estimate sensor positions. We illustrate the impact of inside attacks on the existing algorithms and report simulation results concerning our algorithm. |
URL | http://doi.acm.org/10.1145/2897845.2897926 |
DOI | 10.1145/2897845.2897926 |
Citation Key | won_inside_2016 |