Recovery-by-Learning: Restoring Autonomous Cyber-physical Systems from Sensor Attacks

Francis Akowuah, Romesh Prasad, Carlos Omar Espinoza, Fanxin Kong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 27th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages61-66
Number of pages6
ISBN (Electronic)9781665441889
DOIs
StatePublished - Aug 2021
Event27th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2021 - Virtual, Houston, United States
Duration: Aug 18 2021Aug 20 2021

Publication series

NameProceedings - 2021 IEEE 27th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2021

Conference

Conference27th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2021
Country/TerritoryUnited States
CityVirtual, Houston
Period8/18/218/20/21

Keywords

  • Attack recovery
  • Attack resiliency
  • Autonomous cyber-physical systems
  • Sensor attacks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management

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