Learn-to-Respond: Sequence-Predictive Recovery from Sensor Attacks in Cyber-Physical Systems

Mengyu Liu, Lin Zhang, Vir V. Phoha, Fanxin Kong

Research output: Chapter in Book/Entry/PoemConference contribution

2 Scopus citations


While many research efforts on Cyber-Physical System (CPS) security are devoted to attack detection, how to respond to the detected attacks receives little attention. Attack response is essential since serious consequences can be caused if CPS continues to act on the compromised data by the attacks. In this work, we aim at the response to sensor attacks and adapt machine learning techniques to recover CPSs from such attacks. There are, however, several major challenges. i) Cumulative error. Recovery needs to estimate the current state of a physical system (e.g., the speed of a vehicle) in order to know if the system has been driven to a certain state. However, the estimation error accumulates over time in presence of compromised sensors. ii) Timely response. A fast response is needed since slow recovery not only comes with large estimation errors but also may be too late to avoid irreparable consequences. To address these challenges, we propose a novel learning-based solution, named sequence-predictive recovery (or SeqRec). To reduce the estimation error, SeqRec designs the first sequence-to-sequence (Seq2Seq) model to uncover the temporal and spatial dependencies among sensors and control demands, and then uses the model to estimate system states using the trustworthy data logged in history. To achieve an adequate and fast recovery, SeqRec designs the second Seq2Seq model that considers both the current time step using the remaining intact sensors and the future time steps based on a given target state, and embeds the model into a novel recovery control algorithm to drive a physical system back to that state. Experimental results demonstrate that SeqRec can effectively and efficiently recover CPSs from sensor attacks.

Original languageEnglish (US)
Title of host publication44th IEEE Real-Time Systems Symposium, RTSS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages14
ISBN (Electronic)9798350328578
StatePublished - 2023
Event44th IEEE Real-Time Systems Symposium, RTSS 2023 - Taipei, Taiwan, Province of China
Duration: Dec 5 2023Dec 8 2023

Publication series

NameProceedings - Real-Time Systems Symposium
ISSN (Print)1052-8725


Conference44th IEEE Real-Time Systems Symposium, RTSS 2023
Country/TerritoryTaiwan, Province of China


  • cyber-physical systems
  • recovery
  • sensor attacks

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


Dive into the research topics of 'Learn-to-Respond: Sequence-Predictive Recovery from Sensor Attacks in Cyber-Physical Systems'. Together they form a unique fingerprint.

Cite this