Visible to the public Inside Attack Filtering for Robust Sensor Localization

TitleInside Attack Filtering for Robust Sensor Localization
Publication TypeConference Paper
Year of Publication2016
AuthorsWon, Jongho, Bertino, Elisa
Conference NameProceedings of the 11th ACM on Asia Conference on Computer and Communications Security
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4233-9
KeywordsHuman 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.

URLhttp://doi.acm.org/10.1145/2897845.2897926
DOI10.1145/2897845.2897926
Citation Keywon_inside_2016