Comparison of Corrupted Sensor Data Detection Methods in Detecting Stealthy Attacks on Cyber-Physical Systems
| Autor: | Justin Ruths, Geok See Ng, Aditya P. Mathur, Giedre Sabaliauskaite |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Engineering
business.industry Data detection Real-time computing Cyber-physical system 020206 networking & telecommunications 020207 software engineering Context (language use) CUSUM 02 engineering and technology Data modeling 0202 electrical engineering electronic engineering information engineering Robot business |
| Zdroj: | PRDC |
| DOI: | 10.1109/prdc.2017.47 |
| Popis: | Effectiveness of seven methods for detecting stealthy attacks on Cyber Physical Systems (CPS) was investigated using an experimental study. The Amigobot robot was used as the CPS. The experiments were conducted in simulation as well as on the physical robot. Three types of stealthy attacks were implemented: surge, bias, and geometric. Two variations of Cumulative Sum (CUSUM) method for detecting attacks were evaluated: partial and full physics. Four attack scenarios were implemented. Results from the experiments indicate that stealthy attacks could remain undetected by the CUSUM methods for some attack scenarios. In addition to the CUSUM-based methods, a set of five methods to complement CUSUM were implemented and their effectiveness assessed. While the additional methods do improve the effectiveness of CUSUM-based methods, some attacks remained undetected regardless of which method, or a combination of methods, was used for detection due to the amount of variation in sensor measurements between different runs in simulation and in the physical robot. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|