Abstract
An attack detection scheme is proposed to detect data integrity attacks on sensors in Cyber-Physical Systems (CPSs). A combined fingerprint for sensor and process noise is created during the normal operation of the system. Under sensor spoofing attack, noise pattern deviates from the fingerprinted pattern enabling the proposed scheme to detect attacks. To extract the noise (difference between expected and observed value) a representative model of the system is derived. A Kalman filter is used for the purpose of state estimation. By subtracting the state estimates from the real system states, a residual vector is obtained. It is shown that in steady state the residual vector is a function of process and sensor noise. A set of time domain and frequency domain features is extracted from the residual vector. Feature set is provided to a machine learning algorithm to identify the sensor and process. Experiments are performed on two testbeds, a real-world water treatment (SWaT) facility and a water distribution (WADI) testbed. A class of zero-alarm attacks, designed for statistical detectors on SWaT are detected by the proposed scheme. It is shown that a multitude of sensors can be uniquely identified with accuracy higher than 90% based on the noise fingerprint.
Original language | English |
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Title of host publication | ASIACCS 2018 - Proceedings of the 2018 ACM Asia Conference on Computer and Communications Security |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc. |
Pages | 483–497 |
Number of pages | 15 |
ISBN (Electronic) | 9781450355766 |
DOIs | |
Publication status | Published - 29 May 2018 |
Externally published | Yes |
Event | ASIACCS '18: Asia Conference on Computer and Communications Security - Songdo, Korea, Republic of Duration: 4 Jun 2018 → 8 Jun 2018 |
Conference
Conference | ASIACCS '18: Asia Conference on Computer and Communications Security |
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Country/Territory | Korea, Republic of |
City | Songdo |
Period | 4/06/18 → 8/06/18 |
Keywords
- Actuators
- CPS/ICS Security
- Cyber Physical Systems
- Device Fingerprinting
- Physical Attacks
- Security
- Sensors