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Using Channel State Information for Tamper Detection in the Internet of Things

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Published:07 December 2015Publication History

ABSTRACT

The Internet of Things (IoT) is increasingly used for critical applications and securing the IoT has become a major concern. Among other issues it is important to ensure that tampering with IoT devices is detected. Many IoT devices use WiFi for communication and Channel State Information (CSI) based tamper detection is a valid option. Each 802.11n WiFi frame contains a preamble which allows a receiver to estimate the impact of the wireless channel, the transmitter and the receiver on the signal. The estimation result - the CSI - is used by a receiver to extract the transmitted information. However, as the CSI depends on the communication environment and the transmitter hardware, it can be used as well for security purposes. If an attacker tampers with a transmitter it will have an effect on the CSI measured at a receiver. Unfortunately not only tamper events lead to CSI fluctuations; movement of people in the communication environment has an impact too. We propose to analyse CSI values of a transmission simultaneously at multiple receivers to improve distinction of tamper and movement events. A moving person is expected to have an impact on some but not all communication links between transmitter and the receivers. A tamper event impacts on all links between transmitter and the receivers. The paper describes the necessary algorithms for the proposed tamper detection method. In particular we analyse the tamper detection capability in practical deployments with varying intensity of people movement. In our experiments the proposed system deployed in a busy office environment was capable to detect 53% of tamper events (TPR = 53%) while creating zero false alarms (FPR = 0%).

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  1. Using Channel State Information for Tamper Detection in the Internet of Things

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    • Published in

      cover image ACM Other conferences
      ACSAC '15: Proceedings of the 31st Annual Computer Security Applications Conference
      December 2015
      489 pages
      ISBN:9781450336826
      DOI:10.1145/2818000

      Copyright © 2015 ACM

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      New York, NY, United States

      Publication History

      • Published: 7 December 2015

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      Overall Acceptance Rate104of497submissions,21%

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