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LaSa: Location Aware Wireless Security Access Control for IoT Systems

  • Bingxian Lu
  • Lei Wang
  • Jialin Liu
  • Wei Zhou
  • Linlin Guo
  • Myeong-Hun Jeong
  • Shaowen Wang
  • Guangjie Han
Article
  • 185 Downloads

Abstract

IoT (Internet of Things) security has become a severe yet not well solved problem attracting increasing research attention as well as industrial concerns. Location-based access control approaches, such as Wi-Fi geo-fencing, promise to fulfill the needs of preventing unauthorized access to IoT systems. We propose a crowdsourcing method for location aware security access control, namely LaSa, which is able to confine wireless network access inside certain physical areas only using a single commercial Access Point (AP). Specifically, LaSa detects whether a user enters or exits a room by discovering and recognizing the unique signal patterns. It combines the Received Signal Strength (RSS), Channel State Information (CSI), and coarse Angle of Arrival (AoA) data to improve the accuracy of user classification for accessing the wireless network. Real-world experimental results show that LaSa can achieve a 97.0% accuracy of identification of unauthorized users while maintaining a low false blocking rate of authorized users as low as 3.3%. LaSa is designed to be straightforward for integration with commercial APs and deployment to home and business Wi-Fi environments.

Keywords

Access control Machine learning User validation Internet of things 

Notes

Acknowledgment

This work is supported by “the Fundamental Research Funds for the Central Universities” with No. DUT17LAB16, No. DUT2017TB02. This work is also (partially) supported by Tianjin Key Laboratory of Advanced Networking (TANK), School of Computer Science and Technology, Tianjin University, Tianjin China, 300350 and by Open fund of State Key Laboratory of Acoustics (No. SKLA201706).

References

  1. 1.
    Bahl P, Padmanabhan VN (2000) RADAR: an in-building RF-based user location and tracking system. In: IEEE INFOCOMGoogle Scholar
  2. 2.
    Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2:27:1–27:27CrossRefGoogle Scholar
  3. 3.
    Cheng L, Wang J (2016) How can I guard my AP?: non-intrusive user identification for mobile devices using WiFi signals. In: ACM MobiHocGoogle Scholar
  4. 4.
    Chintalapudi K, Padmanabha Iyer A, Padmanabhan VN (2010) Indoor localization without the pain. In: ACM MobiComGoogle Scholar
  5. 5.
    Danev B, Luecken H, Capkun S, El Defrawy K (2010) Attacks on physical-layer identification. In: ACM WiSecGoogle Scholar
  6. 6.
    Guo X, Zhang D, Wu K, Ni LM (2014) MODLoc: localizing multiple objects in dynamic indoor environment. IEEE Transactions on Parallel and Distributed Systems 25(11):2969–2980CrossRefGoogle Scholar
  7. 7.
    Han C, Wu K, Wang Y, Ni LM (2014) WiFall: device-free fall detection by wireless networks. In: IEEE INFOCOMGoogle Scholar
  8. 8.
    Iannucci PA, Netravali R, Goyal AK, Balakrishnan H (2015) Room-area networks. In: ACM HotNetsGoogle Scholar
  9. 9.
    Jiang Z, Xi W, Li X, Tang S, Zhao J, Han J, Zhao K, Wang Z, Xiao B (2014) Communicating is crowdsourcing: Wi-Fi indoor localization with CSI-based speed estimation. J Comput Sci Technol 29(4):589–604CrossRefGoogle Scholar
  10. 10.
    Jiang Z, Zhao J, Li X, Han J, Xi W (2013) Rejecting the attack: source authentication for Wi-Fi management frames using CSI information IEEE INFOCOMGoogle Scholar
  11. 11.
    Kotaru M, Joshi K, Bharadia D, Katti S (2015) SpotFi: decimeter level localization using WiFi. In: ACM SIGCOMMGoogle Scholar
  12. 12.
    Kumar S, Gil S, Katabi D, Rus D (2014) Accurate indoor localization with zero start-up cost. In: ACM MobiComGoogle Scholar
  13. 13.
    Lu B, Zeng Z, Wang L, Peck B, Qiao D, Segal M (2016) Confining Wi-Fi coverage: a crowdsourced method using physical layer information. In: IEEE SECONGoogle Scholar
  14. 14.
    Luo C, Hong H, Chan MC (2014) PiLoc: a self-calibrating participatory indoor localization system. In: IEEE IPSNGoogle Scholar
  15. 15.
    Mariakakis AT, Sen S, Lee J, Kim KH (2014) SAIL: single access point-based indoor localization. In: ACM MobiSysGoogle Scholar
  16. 16.
    Qian K, Wu C, Yang Z, Liu Y, Zhou Z (2014) PADS: passive detection of moving targets with dynamic speed using PHY layer information. In: IEEE ICPADSGoogle Scholar
  17. 17.
    Rai A, Chintalapudi KK, Padmanabhan VN, Sen R (2012) Zee: zero-effort crowdsourcing for indoor localization. In: ACM MobiComGoogle Scholar
  18. 18.
    Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471CrossRefzbMATHGoogle Scholar
  19. 19.
    Sen S, Choudhury RR, Nelakuditi S (2012) SpinLoc: spin once to know your location. In: ACM HotMobileGoogle Scholar
  20. 20.
    Sen S, Choudhury RR, Radunovic B, Minka T (2011) Precise indoor localization using PHY layer information. In: ACM HotNetsGoogle Scholar
  21. 21.
    Sen S, Lee J, Kim KH, Congdon P (2013) Avoiding multipath to revive inbuilding WiFi localization. In: ACM MobiSysGoogle Scholar
  22. 22.
    Sheth A, Seshan S, Wetherall D (2009) Geo-fencing: confining Wi-Fi coverage to physical boundaries. In: Springer Pervasive, LNCS, vol. 5538Google Scholar
  23. 23.
    Sun L, Sen S, Koutsonikolas D (2014) Bringing mobility-awareness to WLANs using PHY layer information. In: ACM CoNEXTGoogle Scholar
  24. 24.
    Tugnait J, Kim H (2010) A channel-based hypothesis testing approach to enhance user authentication in wireless networks. In: IEEE COMSNETSGoogle Scholar
  25. 25.
    Vasisht D, Kumar S, Katabi D (2016) Decimeter-level localization with a single WiFi access point. In: USENIX NSDIGoogle Scholar
  26. 26.
    Wang H, Sen S, Elgohary A, Farid M, Youssef M, Choudhury RR (2012) No need to war-drive: unsupervised indoor localization. In: ACM MobiSysGoogle Scholar
  27. 27.
    Wang J, Fang D, Chen X, Chang L, Tang Z, Xing T, Liu C (2015) A low cost people flow monitoring system for sensing the potential danger. In: ACM MobiComGoogle Scholar
  28. 28.
    Wang Y, Liu J, Chen Y, Gruteser M, Yang J, Liu H (2014) E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures. In: ACM MobiComGoogle Scholar
  29. 29.
    Wang Y, Yang J, Chen Y, Liu H, Gruteser M, Martin RP (2014) Tracking human queues using single-point signal monitoring. In: ACM MobiSysGoogle Scholar
  30. 30.
    Wu C, Yang Z, Zhou Z, Liu X, Liu Y, Cao J (2015) Non-invasive detection of moving and stationary human with WiFi. IEEE Journal on Selected Areas in Communications 33(11):2329–2342CrossRefGoogle Scholar
  31. 31.
    Wu K, Xiao J, Yi Y, Chen D, Luo X, Ni LM (2013) CSI-based indoor localization. IEEE Transactions on Parallel and Distributed Systems 24(7):1300–1309CrossRefGoogle Scholar
  32. 32.
    Wu K, Xiao J, Yi Y, Gao M, Ni LM (2012) Fila: fine-grained indoor localization. In: IEEE INFOCOMGoogle Scholar
  33. 33.
    Xiao L, Greenstein L, Mandayam NB, Trappe W (2008) Using the physical layer for wireless authentication in time-variant channels. IEEE Trans Wirel Commun 7(7):2571–2579CrossRefGoogle Scholar
  34. 34.
    Xie Y, Li Z, Li M (2015) Precise power delay profiling with commodity WiFi. In: ACM MobiComGoogle Scholar
  35. 35.
    Xiong J, Jamieson K (2013) ArrayTrack: a fine-grained indoor location system. In: USENIX NSDIGoogle Scholar
  36. 36.
    Yang S, Dessai P, Verma M, Gerla M (2013) FreeLoc: calibration-free crowdsourced indoor localization. In: IEEE INFOCOMGoogle Scholar
  37. 37.
    Yang Z, Wu C, Liu Y (2012) Locating in fingerprint space: wireless indoor localization with little human intervention. In: ACM MobiComGoogle Scholar
  38. 38.
    Yang Z, Zhou Z, Liu Y (2013) From RSSI to CSI: indoor localization via channel response. ACM Computing Surveys (CSUR) 46(2):25:1–25:32CrossRefzbMATHGoogle Scholar
  39. 39.
    Zeng Y, Pathak PH, Mohapatra P (2015) Analyzing shopper’s behavior through WiFi signals. In: ACM WPAGoogle Scholar
  40. 40.
    Zhou Z, Yang Z, Wu C, Liu Y, Ni LM (2015) On multipath link characterization and adaptation for device-free human detection. In: ACM ICDCSGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Bingxian Lu
    • 1
  • Lei Wang
    • 1
  • Jialin Liu
    • 1
  • Wei Zhou
    • 1
  • Linlin Guo
    • 1
  • Myeong-Hun Jeong
    • 2
  • Shaowen Wang
    • 3
  • Guangjie Han
    • 4
    • 5
  1. 1.Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceSchool of Software Dalian University of TechnologyDalianChina
  2. 2.The Department of Civil EngineeringChosun UniversityGwangjuRepublic of Korea
  3. 3.Geography and Geographic, Information Science, CyberGIS Center for Advanced, Digital and Spatial Studies, National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  4. 4.College of Internet of Things EngineeringHohai UniversityChangzhouChina
  5. 5.State Key Laboratory of AcousticsInstitute of Acoustics Chinese Academy of SciencesBeijingChina

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