Safety Envelope for Security

Ashish Tiwari, Bruno Dutertre, Dejan Jovanovic, Thomas de Candia, Patrick Lincoln, John M. Rushby, Dorsa Sadigh, and Sanjit A. Seshia. Safety Envelope for Security. In Proceedings of the 3rd International Conference on High Confidence Networked Systems (HiCoNS), pp. 85–94, April 2014.

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Abstract

We present an approach for detecting sensor spoofing attacks on a cyber-physical system. Our approach consists of two steps. In the first step, we construct a safety envelope of the system. Under nominal conditions (that is, when there are no attacks), the system always stays inside its safety envelope. In the second step, we build an attack detector monitor that executes synchronously with the system and raises an alarm whenever the system state falls outside the safety envelope. We synthesize safety envelopes using a modifed machine learning procedure applied on data collected from the system when it is not under attack. We present experimental results that show effectiveness of our approach, and also validate the several novel features that we introduced in our learning procedure.

BibTeX

@inproceedings{tiwari-hicons14,
  author    = {Ashish Tiwari and Bruno Dutertre and Dejan Jovanovic and Thomas de Candia and Patrick Lincoln and John M. Rushby and Dorsa Sadigh and Sanjit A. Seshia},
  title     = {Safety Envelope for Security},
 booktitle = {Proceedings of the 3rd International Conference on High Confidence Networked Systems (HiCoNS)},
 month = "April",
 year = {2014},
  pages = {85--94},
 abstract={We present an approach for detecting sensor spoofing attacks on a cyber-physical system. Our approach consists of 
two steps. In the first step, we construct a safety envelope of 
the system. Under nominal conditions (that is, when there 
are no attacks), the system always stays inside its safety envelope. In the second step, we build an attack detector monitor that executes synchronously with the system and raises 
an alarm whenever the system state falls outside the safety 
envelope. We synthesize safety envelopes using a modifed 
machine learning procedure applied on data collected from 
the system when it is not under attack. We present experimental results that show effectiveness of our approach, and 
also validate the several novel features that we introduced 
in our learning procedure.},
}

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