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

Research output: Contribution in Book/Report/ProceedingsPaper

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Publication date5/12/2015
Host publicationACSAC 2015 Proceedings of the 31st Annual Computer Security Applications Conference
Place of PublicationNew York
PublisherACM
Pages131-140
Number of pages10
ISBN (Print)9781450336826
<mark>Original language</mark>English
EventACSAC 31 2015 Annual Computer Security Applications Conference - Los Angeles, United States

Conference

ConferenceACSAC 31 2015 Annual Computer Security Applications Conference
CountryUnited States
CityLos Angeles
Period7/12/1511/12/15

Conference

ConferenceACSAC 31 2015 Annual Computer Security Applications Conference
CountryUnited States
CityLos Angeles
Period7/12/1511/12/15

Abstract

Each 802.11n WiFi frame contains a preamble which allows a receiver to estimate the impact of the wireless channel and of the transmitter on the received 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 as well be used for security purposes. If an attacker tampers with a transmitter it will have an effect on the CSI measured at a receiver. Many IoT devices use WiFi for communication and CSI based tamper detection is a valuable building block for securing the future IoT. 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 has an impact on some but not all communication links between transmitter and the receivers. A temper 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. For example, in our experiments with low movement intensity it was possible to detect all tamper situations (TPR of one) while achieving a zero FPR.