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1. Integrated instruction set randomization and control reconfiguration for securing cyber-physical systems

   https://doi.org/10.1145/3190619.3190636  

   Potteiger, Bradley; Zhang, Zhenkai; Koutsoukos, Xenofon (April 2018, HoTSoS '18 Proceedings of the 5th Annual Symposium and Bootcamp on Hot Topics in the Science of Security)

   Cyber-Physical Systems (CPS) have been increasingly subject to cyber-attacks including code injection attacks. Zero day attacks further exasperate the threat landscape by requiring a shift to defense in depth approaches. With the tightly coupled nature of cyber components with the physical domain, these attacks have the potential to cause significant damage if safety-critical applications such as automobiles are compromised. Moving target defense techniques such as instruction set randomization (ISR) have been commonly proposed to address these types of attacks. However, under current implementations an attack can result in system crashing which is unacceptable in CPS. As such, CPS necessitate proper control reconfiguration mechanisms to prevent a loss of availability in system operation. This paper addresses the problem of maintaining system and security properties of a CPS under attack by integrating ISR, detection, and recovery capabilities that ensure safe, reliable, and predictable system operation. Specifically, we consider the problem of detecting code injection attacks and reconfiguring the controller in real-time. The developed framework is demonstrated with an autonomous vehicle case study. 

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

   https://doi.org/10.1109/RTSS59052.2023.00017  

   Liu, Mengyu; Zhang, Lin; Phoha, Vir V.; Kong, Fanxin (December 2023, IEEE Real-Time Systems Symposium (RTSS))

   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. 

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3. Constricting Tubes for Prescribed-Time Safe Control

   https://doi.org/10.1109/LCSYS.2026.3701849  

   Gadginmath, Darshan; Allibhoy, Ahmed; Pasqualetti, Fabio (January 2026, IEEE Control Systems Letters)

   We propose a constricting Control Barrier Function (CBF) framework for prescribed-time control of control-affine systems with input constraints. Given a system starting outside a target safe set, we construct a time-varying safety tube that shrinks from a relaxed set containing the initial condition to the target set at a user-specified deadline. Any controller rendering this tube forward invariant guarantees prescribed-time recovery by construction. The constriction schedule is bounded and tunable by design, in contrast to prescribed-time methods where control effort diverges near the deadline. Feasibility under input constraints reduces to a single verifiable condition on the constriction rate, yielding a closed-form minimum recovery time as a function of control authority and initial violation. The framework imposes a single affine constraint per timestep regardless of state dimension, scaling to settings where grid-based reachability methods are intractable. We validate on an 18-dimensional multi-agent system, demonstrating scalability and prescribed-time recovery with bounded control effort. 

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4. Attack Recovery for Cyber-Physical Systems

   Kong, Fanxin; Wang, Zifan (October 2023, EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles)

   Cyber-physical systems (CPSs) rely on computing components to control physical objects, and have been widely used in real-world life-critical applications. However, a CPS has security risks by nature due to the integration of many vulnerable subsystems, which adversaries exploit to inflict serious consequences. Among various attacks, sensor attacks pose a particularly significant threat, where an attacker maliciously modifies sensor measurements to drift system behavior. There is a lot of work in sensor attack prevention and detection. Nevertheless, an essential problem is overlooked: recovery--what to do after detecting a sensor attack, which needs to safely and timely bring a CPS back. We aim to highlight the need to investigate this problem, outline its four key challenges, and provide a brief overview of initial solutions in the field. 

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5. Deadline-Safe Control Synthesis for Cyber-Physical Systems with Reinforcement Learning

   https://doi.org/10.1145/3815110  

   Liu, Mengyu; Lu, Pengyuan; Jiang, Shixiong; Chen, Xin; Sokolsky, Oleg; Lee, Insup; Kong, Fanxin (May 2026, ACM Transactions on Embedded Computing Systems)

   Cyber-physical systems (CPS) play a pivotal role in industrial automation, transportation, and critical infrastructure, where meeting stringent timing constraints is essential to ensure operational safety and efficiency. While reinforcement learning (RL) has shown promise in synthesizing controllers for time-critical applications, existing approaches often prioritize speed (as soon as possible, or ASAP) without explicitly addressing deadline compliance. This misalignment can lead to unsafe or suboptimal behaviors, which are unacceptable in industrial contexts requiring both safety and reliability under hard deadlines. For example, with inappropriate rewards, a control policy for a industrial robot can be encouraged to be less safe but fast instead of being steady and meeting the deadlines. To address this challenge, we investigate the relationship between ASAP behavior and deadline-safe behavior, introducing a novel Markov decision process formulation (R-MDP) that includes time-awareness while preserving the Markov property. We propose a reward design method that systematically encourages deadline compliance and guarantees safety in reach-avoid tasks. Our approach is validated on multiple benchmarks, including linear and nonlinear systems representative of industrial applications, such as DC motor control and real-time attitude control. Experimental results demonstrate the efficacy of our method in achieving deadline-safe control while maintaining system safety, offering a reliable solution for industrial CPS where failure to meet deadlines can have severe consequences. 

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