Secure Indoor Positioning Against Signal Strength Attacks Via Optimized Multi-voting

Indoor positioning systems (IPSes) can enable many location-based services in large indoor venues where GPS signals are unavailable or unreliable. Among the most viable types of IPSes, RSS-IPSes rely on ubiquitous smartphones and indoor WiFi infrastructures and explore distinguishable received signal strength (RSS) measurements at different indoor locations as their location fingerprints. RSS-IPSes are unfortunately vulnerable to physical-layer RSS attacks that cannot be thwarted by conventional cryptographic techniques. Existing defenses against RSS attacks are all subject to an inherent tradeoff between indoor positioning accuracy and attack resilience. This paper presents the design and evaluation of MV-IPS, a novel RSS-IPS based on weighted multi-voting, which does not suffer from this tradeoff. In MV-IPS, every WiFi access point (AP) that receives a user's RSS measurement gives a weighted vote for every reference location, and the reference location that receives the highest accumulative votes from all APs is output as the user's most likely position. Trace-driven simulation studies based on real RSS measurements demonstrate that MV-IPS can achieve much higher positioning accuracy than prior solutions no matter whether RSS attacks are present.

[1]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization via ambience fingerprinting , 2009, MobiCom '09.

[2]  Konstantinos N. Plataniotis,et al.  Sensor selection for mitigation of RSS-based attacks in wireless local area network positioning , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Kaishun Wu,et al.  CSI-Based Indoor Localization , 2013, IEEE Transactions on Parallel and Distributed Systems.

[4]  Paul H. Calamai,et al.  Projected gradient methods for linearly constrained problems , 1987, Math. Program..

[5]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[6]  Luis E. Ortiz,et al.  WiGEM: a learning-based approach for indoor localization , 2011, CoNEXT '11.

[7]  Shih-Hau Fang,et al.  Attack-Resistant Wireless Localization Using an Inclusive Disjunction Model , 2012, IEEE Transactions on Communications.

[8]  Zheng Yang,et al.  The Death and Rebirth of Privacy-Preserving WiFi Fingerprint Localization with Paillier Encryption , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[9]  Yingying Chen,et al.  Robust wireless localization to attacks on access points , 2009, 2009 IEEE Sarnoff Symposium.

[10]  Limin Sun,et al.  Achieving privacy preservation in WiFi fingerprint-based localization , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[11]  Tao Li,et al.  Secure Crowdsourced Indoor Positioning Systems , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[12]  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).

[13]  L. Armijo.  Minimization of functions having Lipschitz continuous first partial derivatives. , 1966 .

[14]  Shiwen Mao,et al.  DeepFi: Deep learning for indoor fingerprinting using channel state information , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

[15]  Neil D. Lawrence,et al.  WiFi-SLAM Using Gaussian Process Latent Variable Models , 2007, IJCAI.

[16]  Feng-Li Lian,et al.  WLAN location determination in e-home via support vector classification , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[17]  M. Sharir,et al.  Online conflict-free coloring for halfplanes, congruent disks, and axis-parallel rectangles , 2009, TALG.

[18]  Xin Pan,et al.  ARIEL: automatic wi-fi based room fingerprinting for indoor localization , 2012, UbiComp.

[19]  Shiwen Mao,et al.  CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach , 2016, IEEE Transactions on Vehicular Technology.

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

[21]  A. S. Krishnakumar,et al.  Bayesian indoor positioning systems , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[22]  Andrew B. Whinston,et al.  Sophisticated voting with information for two voting functions , 1977 .

[23]  V. Padmanabhan,et al.  Enhancements to the RADAR User Location and Tracking System , 2000 .

[24]  Shueng-Han Gary Chan,et al.  Sectjunction: Wi-Fi indoor localization based on junction of signal sectors , 2014, 2014 IEEE International Conference on Communications (ICC).

[25]  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..

[26]  Chao Yang,et al.  Active User-Side Evil Twin Access Point Detection Using Statistical Techniques , 2012, IEEE Transactions on Information Forensics and Security.

[27]  Ha H. Nguyen,et al.  Distributed Linear Constellation Precoding With BICM/BICM-ID in Two-Way Relaying Communications , 2017, IEEE Transactions on Vehicular Technology.

[28]  José Manuel Páez-Borrallo,et al.  A New Location Estimation System for Wireless Networks Based on Linear Discriminant Functions and Hidden Markov Models , 2006, EURASIP J. Adv. Signal Process..

[29]  Yanchao Zhang,et al.  Secure RSS-Fingerprint-Based Indoor Positioning: Attacks and Countermeasures , 2018, 2018 IEEE Conference on Communications and Network Security (CNS).

[30]  Tom Minka,et al.  You are facing the Mona Lisa: spot localization using PHY layer information , 2012, MobiSys '12.