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ZigBee Device Verification for Securing Industrial Control and Building Automation Systems

Abstract : Improved wireless ZigBee network security provides a means to mitigate malicious network activity due to unauthorized devices. Security enhancement using RF-based features can augment conventional bit-level security approaches that are solely based on the MAC addresses of ZigBee devices. This paper presents a device identity verification process using RF fingerprints from like-model CC2420 2.4 GHz ZigBee device transmissions in operational indoor scenarios involving line-of-sight and through-wall propagation channels, as well as an anechoic chamber representing near-ideal conditions. A trained multiple discriminant analysis model was generated using normalized multivariate Gaussian test statistics from authorized network devices. Authorized device classification and ID verification were assessed using pre-classification Kolmogorov-Smirnov (KS) feature ranking and post-classification generalized relevance learning vector quantization improved (GRLVQI) relevance ranking. A true verification rate greater than 90% and a false verification rate less than 10% were obtained when assessing authorized device IDs. When additional rogue devices were introduced that attempted to gain unauthorized network access by spoofing the bit-level credentials of authorized devices, the KS-test feature set achieved a true verification rate greater than 90% and a rogue reject rate greater than 90% in 29 of 36 rogue scenarios while the GRLVQI feature set was successful in 28 of 36 scenarios.
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Clay Dubendorfer, Benjamin Ramsey, Michael Temple. ZigBee Device Verification for Securing Industrial Control and Building Automation Systems. 7th International Conference on Critical Infrastructure Protection (ICCIP), Mar 2013, Washington, DC, United States. pp.47-62, ⟨10.1007/978-3-642-45330-4_4⟩. ⟨hal-01456892⟩

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