NoisePrint: Attack detection using sensor and process noise fingerprint in cyber physical systems
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 p...
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Acceso en línea: | https://repository.urosario.edu.co/handle/10336/23055 https://doi.org/10.1145/3196494.3196532 |
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ir-10336-230552022-05-02T12:37:14Z NoisePrint: Attack detection using sensor and process noise fingerprint in cyber physical systems Ahmed C.M. Qadeer R. Ochoa M. Murguia C. Zhou J. Mathur A.P. Ruths J. Actuators Cyber Physical System Embedded systems Frequency domain analysis Learning algorithms Learning systems Sensors State estimation Testbeds Water supply systems Water treatment CPS/ICS Security Cyber physical systems (cpss) Data integrity attacks Device fingerprinting Frequency domains Physical attacks Security Water distributions Palmprint recognition Actuators CPS/ICS Security Cyber Physical Systems Device Fingerprinting Physical Attacks Security Sensors 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. © 2018 Association for Computing Machinery. 2018 2020-05-25T23:59:30Z info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion https://repository.urosario.edu.co/handle/10336/23055 https://doi.org/10.1145/3196494.3196532 eng info:eu-repo/semantics/openAccess application/pdf Association for Computing Machinery, Inc instname:Universidad del Rosario |
institution |
EdocUR - Universidad del Rosario |
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language |
Inglés (English) |
topic |
Actuators Cyber Physical System Embedded systems Frequency domain analysis Learning algorithms Learning systems Sensors State estimation Testbeds Water supply systems Water treatment CPS/ICS Security Cyber physical systems (cpss) Data integrity attacks Device fingerprinting Frequency domains Physical attacks Security Water distributions Palmprint recognition Actuators CPS/ICS Security Cyber Physical Systems Device Fingerprinting Physical Attacks Security Sensors |
spellingShingle |
Actuators Cyber Physical System Embedded systems Frequency domain analysis Learning algorithms Learning systems Sensors State estimation Testbeds Water supply systems Water treatment CPS/ICS Security Cyber physical systems (cpss) Data integrity attacks Device fingerprinting Frequency domains Physical attacks Security Water distributions Palmprint recognition Actuators CPS/ICS Security Cyber Physical Systems Device Fingerprinting Physical Attacks Security Sensors Ahmed C.M. Qadeer R. Ochoa M. Murguia C. Zhou J. Mathur A.P. Ruths J. NoisePrint: Attack detection using sensor and process noise fingerprint in cyber physical systems |
description |
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. © 2018 Association for Computing Machinery. |
format |
Objeto de conferencia (Conference Object) |
author |
Ahmed C.M. Qadeer R. Ochoa M. Murguia C. Zhou J. Mathur A.P. Ruths J. |
author_facet |
Ahmed C.M. Qadeer R. Ochoa M. Murguia C. Zhou J. Mathur A.P. Ruths J. |
author_sort |
Ahmed C.M. |
title |
NoisePrint: Attack detection using sensor and process noise fingerprint in cyber physical systems |
title_short |
NoisePrint: Attack detection using sensor and process noise fingerprint in cyber physical systems |
title_full |
NoisePrint: Attack detection using sensor and process noise fingerprint in cyber physical systems |
title_fullStr |
NoisePrint: Attack detection using sensor and process noise fingerprint in cyber physical systems |
title_full_unstemmed |
NoisePrint: Attack detection using sensor and process noise fingerprint in cyber physical systems |
title_sort |
noiseprint: attack detection using sensor and process noise fingerprint in cyber physical systems |
publisher |
Association for Computing Machinery, Inc |
publishDate |
2018 |
url |
https://repository.urosario.edu.co/handle/10336/23055 https://doi.org/10.1145/3196494.3196532 |
_version_ |
1740172624756998144 |
score |
12,131701 |