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PHY-IDS: a physical-layer spoofing attack detection system for wearable devices

Published: 19 June 2020 Publication History

Abstract

In modern connected healthcare applications, wearable devices supporting real-time monitoring and diagnosis have become mainstream. However, wearable systems are exposed to massive cyberattacks that threaten not only data security but also human safety and life. One of the fundamental security threats is device impersonation. We therefore propose PHY-IDS; a lightweight real-time detection system that captures spoofing attacks leveraging on body motions. Our system utilizes time series of physical layer features and builds on the fact that it is non-trivial to inject malicious frames that are indistinguishable with legitimate ones. With the help of statistical learning, our system characterizes the signal behavior and flags deviations as anomalies. We experimentally evaluate PHY-IDS's performance using bodyworn devices in real attack scenarios. For four types of attackers with increasing knowledge of the deployed detection system, the results show that PHY-IDS detects naive attackers with high accuracy above 99.8\% and maintains good accuracy for stronger attackers at a range from 81.0% to 98.9%.

References

[1]
Yingying Chen, Wade Trappe, and Richard P Martin. 2007. Detecting and localizing wireless spoofing attacks. In 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).
[2]
Miniutti Dino and et al. 2008. Narrowband Channel Characterization for Body Area Networks. Technical Report.
[3]
Daniel B Faria and David R Cheriton. 2006. Detecting identity-based attacks in wireless networks using signalprints. In Proceedings of the 5th ACM workshop on Wireless security.
[4]
Ernst Haselsteiner and Klemens Breitfuß. 2006. Security in near field communication (NFC). In Workshop on RFID security.
[5]
Yong Huang, Mengnian Xu, Wei Wang, Hao Wang, Tao Jiang, and Qian Zhang. 2019. Towards motion invariant authentication for on-body IoT devices. In IEEE International Conference on Communications (ICC).
[6]
Zhiping Jiang, Jizhong Zhao, Xiang-Yang Li, Jinsong Han, and Wei Xi. 2013. Rejecting the attack: Source authentication for wi-fi management frames using csi information. In IEEE Conference on Computer Communications (INFOCOM).
[7]
Pengfei Liu and et al. 2019. Real-time Identification of Rogue WiFi Connections Using Environment-Independent Physical Features. In IEEE Conference on Computer Communications (INFOCOM).
[8]
Lily Hay Newman. 2019. A Model Hospital Where the Devices Get Hacked on Purpose. https://www.wired.com/story/defcon-medical-device-village-hacking-hospital/ Retrieved April 14, 2020 from
[9]
Kasper Bonne Rasmussen, Claude Castelluccia, Thomas S Heydt-Benjamin, and Srdjan Capkun. 2009. Proximity-based access control for implantable medical devices. In Proceedings of the 16th ACM conference on Computer and communications security (CCS).
[10]
Girish Revadigar, Chitra Javali, Wen Hu, and Sanjay Jha. 2015. DLINK: Dual link based radio frequency fingerprinting for wearable devices. In IEEE 40th Conference on Local Computer Networks (LCN).
[11]
Yong Sheng, Keren Tan, Guanling Chen, David Kotz, and Andrew Campbell. 2008. Detecting 802.11 MAC layer spoofing using received signal strength. In IEEE Conference on Computer Communications (INFOCOM).
[12]
Lu Shi, Jiawei Yuan, Shucheng Yu, and Ming Li. 2015. MASK-BAN: Movement-aided authenticated secret key extraction utilizing channel characteristics in body area networks. IEEE Internet of Things Journal (2015).
[13]
Kannan Srinivasan and Philip Levis. 2006. RSSI is under appreciated. In Proceedings of the 3rd workshop on embedded networked sensors.
[14]
Stéphane Van Roy and et al. 2012. Dynamic channel modeling for multi-sensor body area networks. IEEE Transactions on Antennas and Propagation 61, 4 (2012).
[15]
Meng Zhang, Anand Raghunathan, and Niraj K Jha. 2013. MedMon: Securing medical devices through wireless monitoring and anomaly detection. IEEE Transactions on Biomedical circuits and Systems (2013).

Cited By

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  • (2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 8-Feb-2024
  • (2023)A Secure Wearable Framework for Stress Detection in Patients Affected by Communicable DiseasesIEEE Sensors Journal10.1109/JSEN.2022.320458623:2(981-988)Online publication date: 15-Jan-2023
  • (2023)A Lightweight Intrusion Detection System against IoT Memory Corruption Attacks2023 26th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)10.1109/DDECS57882.2023.10139718(118-123)Online publication date: 3-May-2023
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  1. PHY-IDS: a physical-layer spoofing attack detection system for wearable devices

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      cover image ACM Conferences
      WearSys '20: Proceedings of the 6th ACM Workshop on Wearable Systems and Applications
      June 2020
      42 pages
      ISBN:9781450380133
      DOI:10.1145/3396870
      • Program Chairs:
      • Vu Tran,
      • Ashwin Ashok
      © 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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      Publication History

      Published: 19 June 2020

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      Author Tags

      1. machine learning
      2. physical-layer security
      3. spoofing attacks
      4. time series analysis
      5. wearables

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      • Stiftelsen för Strategisk Forskning

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      Overall Acceptance Rate 28 of 36 submissions, 78%

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      Cited By

      View all
      • (2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 8-Feb-2024
      • (2023)A Secure Wearable Framework for Stress Detection in Patients Affected by Communicable DiseasesIEEE Sensors Journal10.1109/JSEN.2022.320458623:2(981-988)Online publication date: 15-Jan-2023
      • (2023)A Lightweight Intrusion Detection System against IoT Memory Corruption Attacks2023 26th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)10.1109/DDECS57882.2023.10139718(118-123)Online publication date: 3-May-2023
      • (2023)Implicit IoT authentication using on-phone ANN models and breathing dataInternet of Things10.1016/j.iot.2023.10100324(101003)Online publication date: Dec-2023
      • (2022)Towards Spoofing Resistant Next Generation IoT NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2022.317027617(1669-1683)Online publication date: 2022
      • (2022)Towards an information-theoretic framework of intrusion detection for composed systems and robustness analysesComputers and Security10.1016/j.cose.2022.102633116:COnline publication date: 1-May-2022
      • (2021)Recent Advances in Wearable Sensing TechnologiesSensors10.3390/s2120682821:20(6828)Online publication date: 14-Oct-2021

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