Robust wireless localization to attacks on access points

Trustworthy location information is important because it is a critical input to a wide variety of location-based applications. However, the localization infrastructure is vulnerable to physical attacks and consequently the localization results are affected. In this paper, we focus on achieving robust wireless localization when attacks are present on access points. We first investigate the effects of attacks on localization. We then derive an attack-resistant scheme that can be integrated with existing localization algorithms and are not algorithm-specific. Our attack-resistant scheme are based on K-means clustering analysis. We examined our approach using received signal strength (RSS) in widely used lateration-based algorithms. We validated our method in the ORBIT testbed with an IEEE 802.11 (Wi-Fi) network. Our experimental results demonstrate that our proposed approach can achieve comparable localization performance when under access-point attacks as compared to normal situations without attack.

[1]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.

[2]  Srdjan Capkun,et al.  Secure Localization with Hidden and Mobile Base Stations , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[3]  Koen Langendoen,et al.  Distributed localization in wireless sensor networks: a quantitative compariso , 2003, Comput. Networks.

[4]  Srdjan Capkun,et al.  Secure positioning of wireless devices with application to sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[5]  Wade Trappe,et al.  Robust statistical methods for securing wireless localization in sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[6]  Donggang Liu,et al.  Attack-resistant location estimation in sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[7]  Jie Yang,et al.  A theoretical analysis of wireless localization using RF-based fingerprint matching , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[8]  Richard P. Martin,et al.  A security and robustness performance analysis of localization algorithms to signal strength attacks , 2009, TOSN.

[9]  Alfred O. Hero,et al.  Locating the Nodes , 2005 .

[10]  Tarek F. Abdelzaher,et al.  Range-free localization schemes for large scale sensor networks , 2003, MobiCom '03.

[11]  Richard P. Martin,et al.  A Practical Approach to Landmark Deployment for Indoor Localization , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[12]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[13]  R.L. Moses,et al.  Locating the nodes: cooperative localization in wireless sensor networks , 2005, IEEE Signal Processing Magazine.

[14]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[15]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[16]  B. R. Badrinath,et al.  Ad hoc positioning system (APS) , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[17]  David A. Wagner,et al.  Secure verification of location claims , 2003, WiSe '03.

[18]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .