TY - GEN
T1 - Recovery-by-Learning
T2 - 27th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2021
AU - Akowuah, Francis
AU - Prasad, Romesh
AU - Espinoza, Carlos Omar
AU - Kong, Fanxin
N1 - Funding Information:
We would like to thank the anonymous reviewers for their constructive comments. This work was supported in part by NSF CCF-2028740.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - Autonomous cyber-physical systems (CPS) are susceptible to non-invasive physical attacks such as sensor spoofing attacks that are beyond the classical cybersecurity domain. These attacks have motivated numerous research efforts on attack detection, but little attention on what to do after detecting an attack. The importance of attack recovery is emphasized by the need to mitigate the attack's impact on a system and restore it to continue functioning. There are only a few works addressing attack recovery, but they all rely on prior knowledge of system dynamics. To overcome this limitation, we propose Recovery-by-Learning, a data-driven attack recovery framework that restores CPS from sensor attacks. The framework leverages natural redundancy among heterogeneous sensors and historical data for attack recovery. Specially, the framework consists of two major components: state predictor and data checkpointer. First, the predictor is triggered to estimate systems states after the detection of an attack. We propose a deep learning-based prediction model that exploits the temporal correlation among heterogeneous sensors. Second, the checkpointer executes when no attack is detected. We propose a double sliding window based checkpointing protocol to remove compromised data and keep trustful data as input to the state predictor. Third, we implement and evaluate the effectiveness of our framework using a realistic data set and a ground vehicle simulator. The results show that our method restores a system to continue functioning in presence of sensor attacks.
AB - Autonomous cyber-physical systems (CPS) are susceptible to non-invasive physical attacks such as sensor spoofing attacks that are beyond the classical cybersecurity domain. These attacks have motivated numerous research efforts on attack detection, but little attention on what to do after detecting an attack. The importance of attack recovery is emphasized by the need to mitigate the attack's impact on a system and restore it to continue functioning. There are only a few works addressing attack recovery, but they all rely on prior knowledge of system dynamics. To overcome this limitation, we propose Recovery-by-Learning, a data-driven attack recovery framework that restores CPS from sensor attacks. The framework leverages natural redundancy among heterogeneous sensors and historical data for attack recovery. Specially, the framework consists of two major components: state predictor and data checkpointer. First, the predictor is triggered to estimate systems states after the detection of an attack. We propose a deep learning-based prediction model that exploits the temporal correlation among heterogeneous sensors. Second, the checkpointer executes when no attack is detected. We propose a double sliding window based checkpointing protocol to remove compromised data and keep trustful data as input to the state predictor. Third, we implement and evaluate the effectiveness of our framework using a realistic data set and a ground vehicle simulator. The results show that our method restores a system to continue functioning in presence of sensor attacks.
KW - Attack recovery
KW - Attack resiliency
KW - Autonomous cyber-physical systems
KW - Sensor attacks
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U2 - 10.1109/RTCSA52859.2021.00015
DO - 10.1109/RTCSA52859.2021.00015
M3 - Conference contribution
AN - SCOPUS:85115828977
T3 - Proceedings - 2021 IEEE 27th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2021
SP - 61
EP - 66
BT - Proceedings - 2021 IEEE 27th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 August 2021 through 20 August 2021
ER -