Tiwari, A., Dutertre, B., Jovanovi, D., de Candia, T., Lincoln, P. D., Rushby, J., . . . Seshia, S. (2014, 15-17 April). Safety envelope for security. Paper presented at the International Conference on High Confidence Networked Systems (HiCoNS ’14), Berlin, Germany.
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: a 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.
Index terms:
- Computing methodologies
- Machine learning
- Machine learning approaches
- Factorization methods
- Canonical correlation analysis
- Factorization methods
- Machine learning approaches
- Machine learning
- Information systems
- Data management systems
- Middleware for databases
- Distributed transaction monitors
- Middleware for databases
- Data management systems
- Mathematics of computing
- Probability and statistics
- Statistical paradigms
- Regression analysis
- Statistical paradigms
- Probability and statistics