Real-time Attack-recovery for Cyber-physical Systems Using Linear-quadratic Regulator

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-24
Author(s):  
Lin Zhang ◽  
Pengyuan Lu ◽  
Fanxin Kong ◽  
Xin Chen ◽  
Oleg Sokolsky ◽  
...  
Keyword(s):  
Real Time ◽  
Current System ◽  
Linear Quadratic Regulator ◽  
Cyber Physical Systems ◽  
System State ◽  
Linear Quadratic ◽  
Security Vulnerabilities ◽  
Physical Systems ◽  
Alternating Direction ◽  
Safety Constraints

The increasing autonomy and connectivity in cyber-physical systems (CPS) come with new security vulnerabilities that are easily exploitable by malicious attackers to spoof a system to perform dangerous actions. While the vast majority of existing works focus on attack prevention and detection, the key question is “what to do after detecting an attack?”. This problem attracts fairly rare attention though its significance is emphasized by the need to mitigate or even eliminate attack impacts on a system. In this article, we study this attack response problem and propose novel real-time recovery for securing CPS. First, this work’s core component is a recovery control calculator using a Linear-Quadratic Regulator (LQR) with timing and safety constraints. This component can smoothly steer back a physical system under control to a target state set before a safe deadline and maintain the system state in the set once it is driven to it. We further propose an Alternating Direction Method of Multipliers (ADMM) based algorithm that can fast solve the LQR-based recovery problem. Second, supporting components for the attack recovery computation include a checkpointer, a state reconstructor, and a deadline estimator. To realize these components respectively, we propose (i) a sliding-window-based checkpointing protocol that governs sufficient trustworthy data, (ii) a state reconstruction approach that uses the checkpointed data to estimate the current system state, and (iii) a reachability-based approach to conservatively estimate a safe deadline. Finally, we implement our approach and demonstrate its effectiveness in dealing with totally 15 experimental scenarios which are designed based on 5 CPS simulators and 3 types of sensor attacks.

scholarly journals Modelling, analysis and control design of hybrid dynamical systems

2019 ◽  
Vol 70 (3) ◽  
pp. 176-186
Author(s):  
Dominik Vošček ◽  
Anna Jadlovská ◽  
Dominik Grigl’ák
Keyword(s):  
Hybrid Systems ◽  
Piecewise Linear ◽  
Control Design ◽  
Linear Quadratic Regulator ◽  
Cyber Physical Systems ◽  
Jacobi Matrices ◽  
Hybrid Automata ◽  
Linear Quadratic ◽  
Physical Systems ◽  
And Control

Abstract This paper introduces a methodology for one of the challenges regarding cyber-physical systems, ie modelling and control design them as hybrid systems. The proposed methodology comprises modules with specific steps to accomplish the tasks. Specifically, the paper aims to utilize hybrid systems framework onto the chosen hydraulic hybrid system with complex dynamics to showcase different aspects of hybrid systems. The mathematical model was derived using hybrid automata framework and then transformed into the linear form either using Jacobi matrices or using linear approximations without Jacobi matrices. After that the system was validated and analysed and the control design utilizing piecewise linear-quadratic regulator optimal control was proposed. Furthermore, parameters of control algorithm were tuned using particle swarm optimization algorithm. The whole logic, system dynamics and constrains are implemented within MATLAB/Simulink simulation environment using s -functions. The proposed methodology can be implemented on the various types of cyber-physical systems as far as they can be described as hybrid systems.

scholarly journals Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

2021 ◽  
Vol 35 (2) ◽  
pp. 251-264
Author(s):  
Dimitrios Boursinos ◽  
Xenofon Koutsoukos
Keyword(s):  
Neural Networks ◽  
Distance Learning ◽  
Real Time ◽  
Speaker Recognition ◽  
Deep Neural Networks ◽  
Cyber Physical Systems ◽  
Error Rates ◽  
Traffic Sign ◽  
Conformal Prediction ◽  
Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

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