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    ์ƒ˜ํ”Œ ๋ฐ์ดํ„ฐ๋กœ ํ‘œํ˜„๋˜๋Š” ์‚ฌ์ด๋ฒ„-๋ฌผ๋ฆฌ ์‹œ์Šคํ…œ์˜ ์ทจ์•ฝ์  ๋ถ„์„ ๋ฐ ๊ฒ€์ถœ ๋ถˆ๊ฐ€๋Šฅํ•œ ๊ณต๊ฒฉ์— ๋Œ€ํ•œ ๋ฐฉ์–ด ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. ์‹ฌํ˜•๋ณด.The rapid evolution of communication network and computation speed has led to the emergence of cyber-physical systems in which the traditional physical plants are controlled remotely using digital controllers. Unfortunately, however, the separation between the plant and controller with a network communication provides a new chance for external adversaries to intrude control systems, which are highly connected to human life and social infrastructures. For this reason, among various issues of the cyber-physical system, security problems have gained particular attention to control engineers these days. This dissertation presents new theoretical vulnerabilities undetectable from the conventional anomaly detector, which arise due to the mixture of continuous- and discrete-time components on cyber-physical systems, and addresses countermeasures against such vulnerabilities. Specific subjects dealt with in the dissertation are listed as follows: 1) Zero dynamics attacks can be lethal to cyber-physical systems because they can be harmful to physical plants and impossible to detect. Fortunately, if the given continuous-time physical system is minimum phase, the attack is not so effective even if it cannot be detected. However, the situation can become unfavorable if one uses digital control by sampling the sensor measurement and using a zero-order hold for actuation because of the `sampling zeros.' When the continuous-time system has a relative degree greater than two and the sampling period is small, the sampled-data system must have unstable zeros, so that the cyber-physical system becomes vulnerable to `sampling zero dynamics attack.' In this dissertation, we present an idea to neutralize the zero dynamics attack for single-input and single-output sampled-data systems by shifting the unstable discrete-time zeros into stable ones. This idea is realized by employing the so-called `generalized hold' which replaces a standard zero-order hold. It is shown that, under mild assumptions, a generalized hold exists which places the discrete-time zeros at desired positions. Furthermore, we formulate the design problem as an optimization problem whose performance index is related to the inter-sample behavior of the physical plant, and propose an optimal gain which alleviates the performance degradation caused by generalized hold as much as possible, and in order to verify the theoretical results, we apply the proposed strategy to a DC/DC converter with an electrical circuit. 2) The zero dynamics attack has usually been studied as a type of actuator attack, but it can harm the physical plant through the sensor network. Specifically, when the system monitors abnormal behavior of the plant using the anomaly detector (fault detector), one can generate zero dynamics attack on the sensor network deceiving the anomaly detector by regarding the output of the plant and residual of the anomaly detector as a new input and output of a target system. It is noticed that this sensor attack is not so effective when the plant is stable even if the attack is still undetectable. Noting this point, we propose to reexamine the generalized hold as a countermeasure against the undetectable sensor attack. That is, using the fact that the output feedback passing through the generalized hold can stabilize the unstable systems by selecting an appropriate hold function, we show that the plant can be safe from the undetectable sensor attack. Furthermore, to relieve the performance degradation of the use of generalized hold feedback, we employ a discrete-time linear quadratic regulator minimizing a continuous-time cost function. 3) In the sampled-data framework, most anomaly detectors monitor the plant's output only at discrete time instants. Consequently, abnormal behavior between sampling instants cannot be detected if output behaves normally at every sampling instant. This implies that if an actuator attack drives the plant's state to pass through the kernel of the output matrix at each sensing time, then the attack compromises the system while remaining stealthy. This type of attack is always constructible when the sampled-data system has an input redundancy, i.e., the number of inputs being larger than that of outputs and/or the sampling rate of the actuators being higher than that of the sensors. Simulation results for the X-38 vehicle and other numerical examples illustrate this new attack strategy may result in disastrous consequences.๋””์ง€ํ„ธ ์žฅ์น˜๋“ค์˜ ์—ฐ์‚ฐ ์†๋„์™€ ๋„คํŠธ์›Œํฌ ์ „์†ก ์†๋„์˜ ๊ธ‰์ง„์ ์ธ ๋ฐœ์ „์œผ๋กœ ๊ณ ์ „์ ์ธ ์ œ์–ด ์‹œ์Šคํ…œ์ด ๋„คํŠธ์›Œํฌ๋ฅผ ํ†ตํ•ด ์›๊ฒฉ์œผ๋กœ ์ œ์–ด๋˜๋Š” ์‚ฌ์ด๋ฒ„-๋ฌผ๋ฆฌ ์‹œ์Šคํ…œ(cyber-physical systems)์ด ๋“ฑ์žฅํ•˜๊ธฐ ์‹œ์ž‘ํ–ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์‚ฌ์ด๋ฒ„-๋ฌผ๋ฆฌ ์‹œ์Šคํ…œ์€ ์ œ์–ด๊ธฐ์™€ ์ œ์–ด ๋Œ€์ƒ์˜ ๋ถ„๋ฆฌ๋ผ๋Š” ํŠน์„ฑ์ƒ ์™ธ๋ถ€์˜ ์•…์˜์ ์ธ ๊ณต๊ฒฉ์‹ ํ˜ธ๋กœ ๋ถ€ํ„ฐ ๊ณต๊ฒฉ๋‹นํ•  ์ˆ˜ ์žˆ๋Š” ์ž ์žฌ์ ์ธ ์œ„ํ—˜์— ๋…ธ์ถœ๋˜์–ด ์žˆ์œผ๋ฉฐ ํŒŒ์›Œํ”Œ๋žœํŠธ์˜ ์›๊ฒฉ๊ฐ์‹œ์ œ์–ด(SCADA, Supervisory Control And Data Acquisition)์™€ ๊ฐ™์€ ์‚ฌํšŒ ๊ธฐ๋ฐ˜ ์‹œ์„ค๊ณผ๋„ ๋ฐ€์ ‘ํ•œ ์—ฐ๊ด€์ด ์žˆ์–ด ๊ทธ ๋ณด์•ˆ์„ฑ์— ๊ด€ํ•œ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์ด ๊ฐ•์กฐ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์‚ฌ์ด๋ฒ„-๋ฌผ๋ฆฌ ์‹œ์Šคํ…œ์ด ์—ฐ์†์‹œ๊ฐ„์œผ๋กœ ์ด๋ฃจ์–ด์ง„ ๋ฌผ๋ฆฌ ํ”Œ๋žœํŠธ(physical plant)์™€ ๋””์ง€ํ„ธ ์ œ์–ด๊ธฐ๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค๋กœ๋ถ€ํ„ฐ ์ด๋ฅผ ์˜์ฐจํ™€๋“œ(zero-order hold)์™€ ์ƒ˜ํ”Œ๋Ÿฌ(sampler)๋กœ ์ด์‚ฐํ™”(discretize)๋˜๋Š” ์ƒ˜ํ”Œ-๋ฐ์ดํ„ฐ ์‹œ์Šคํ…œ์œผ๋กœ ํ‘œํ˜„ํ•˜๊ณ , ์—ฐ์†์‹œ๊ฐ„๊ณผ ์ด์‚ฐ์‹œ๊ฐ„์˜ ๊ฒฐํ•ฉ์œผ๋กœ ๋ถ€ํ„ฐ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ์‚ฌ์ด๋ฒ„ ๊ณต๊ฒฉ์— ๋Œ€ํ•œ ์ด๋ก ์ ์ธ ์ทจ์•ฝ์ ์„ ๋ถ„์„ํ•˜๊ณ  ๊ทธ์— ๋Œ€ํ•œ ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹ค์Œ์˜ ์„ธ ๊ฐ€์ง€ ์ฃผ์ œ๋“ค์„ ๋‹ค๋ฃฌ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ, ๋ณธ ๋…ผ๋ฌธ์€ ์‹œ์Šคํ…œ์˜ ๋ถˆ์•ˆ์ •ํ•œ(unstable) ์˜์ (zero)์˜ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ์ž…๋ ฅ ๋„คํŠธ์›Œํฌ๋ฅผ ํ†ตํ•ด ์ฃผ์ž…๋  ๊ฒฝ์šฐ ๊ฒ€์ถœ๋ถˆ๊ฐ€๋Šฅ(undetectable)ํ•œ ์˜๋™์—ญํ•™ ๊ณต๊ฒฉ(zero dynamics attack)์ด ์ƒ˜ํ”Œ ๋ฐ์ดํ„ฐ ์‹œ์Šคํ…œ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ƒ˜ํ”Œ๋ง ์˜์ (sampling zero)์„ ์ด์šฉํ•˜์—ฌ๋„ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์ ์„ ๋ฐํžŒ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์˜์ฐจํ™€๋“œ ๋Œ€์‹  ์ผ๋ฐ˜ํ™”๋œ ํ™€๋“œ(generalized hold)๋ฅผ ์ด์šฉํ•  ๊ฒฝ์šฐ ์ด์‚ฐ์‹œ๊ฐ„ ์‹œ์Šคํ…œ์˜ ์ด์‚ฐ์‹œ๊ฐ„ ์˜์ ์„ ๋ชจ๋‘ ์•ˆ์ •ํ•œ(stable)ํ•œ ์˜์—ญ์œผ๋กœ ํ• ๋‹นํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์— ๊ทผ๊ฑฐํ•˜์—ฌ ์˜๋™์—ญํ•™ ๊ณต๊ฒฉ์— ๋Œ€ํ•œ ๊ทผ๋ณธ์ ์ธ ๋Œ€์‘์ฑ…์œผ๋กœ ์˜์ฐจํ™€๋“œ๋ฅผ ์ผ๋ฐ˜ํ™”๋œ ํ™€๋“œ๋กœ ๋Œ€์ฒดํ•˜๋Š” ๋ฐฉ์•ˆ์„ ์ œ์•ˆํ•œ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ, ์ผ๋ฐ˜ํ™”๋œ ํ™€๋“œ๋ฅผ ์ด์šฉํ•  ๊ฒฝ์šฐ ๋ฐœ์ƒํ•˜๋Š” ์„ฑ๋Šฅ์ €ํ•˜๋ฅผ ์ตœ์†Œํ™” ํ•˜๊ธฐ ์œ„ํ•ด ๋ณผ๋ก(convex) ์ตœ์ ํ™” ๋ฌธ์ œ๋กœ ์ผ๋ฐ˜ํ™”๋œ ํ™€๋“œ๋ฅผ ์„ค๊ณ„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋‹ค๋ฅธ ํ•œํŽธ, ์ด์‚ฐ์‹œ๊ฐ„ ์‹œ์Šคํ…œ์˜ ์ถœ๋ ฅ ์„ผ์„œ ๋„คํŠธ์›Œํฌ๋ฅผ ์ž…๋ ฅ ๊ทธ๋ฆฌ๊ณ  ๊ณ ์žฅ ๊ฒ€์ถœ๊ธฐ(fault detector)์˜ ์ž”์—ฌ์‹ ํ˜ธ(residual)๋ฅผ ์ถœ๋ ฅ์œผ๋กœ ํ•˜๋Š” ์‹œ์Šคํ…œ์˜ ์˜๋™์—ญํ•™์„ ์ด์šฉํ•˜์—ฌ ๊ฒ€์ถœ ๋ถˆ๊ฐ€๋Šฅํ•œ ์„ผ์„œ ๊ณต๊ฒฉ์ด ๊ฐ€๋Šฅํ•จ์„ ๋ณด์ด๊ณ , ์ด์— ๋Œ€ํ•œ ํ•ด๊ฒฐ์ฑ…์œผ๋กœ ์ด์‚ฐ์‹œ๊ฐ„ ์ถœ๋ ฅ ๋ถ€ํ„ฐ ์—ฐ์†์‹œ๊ฐ„ ์ž…๋ ฅ๊นŒ์ง€ ์ผ๋ฐ˜ํ™”๋œ ํ™€๋“œ๋ฅผ ์ด์šฉํ•œ ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„๋ฅผ ์ถ”๊ฐ€ํ•˜์—ฌ ๊ณต๊ฒฉ์˜ ํšจ๊ณผ๋ฅผ ๋ฌดํšจํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ ์ด๋Ÿฌํ•œ ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„๋กœ ์ธํ•œ ์ œ์–ด ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ์ตœ์†Œํ™”ํ•˜๊ธฐ ์œ„ํ•ด ์—ฐ์†์‹œ๊ฐ„ ๋น„์šฉํ•จ์ˆ˜๋ฅผ ์ตœ์†Œํ™”ํ•˜๋Š” ์ด์‚ฐ์‹œ๊ฐ„ ์ตœ์  ์ œ์–ด๊ธฐ๋ฒ•์˜ ์ด์šฉ์„ ์ œ์•ˆํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์˜์ฐจํ™€๋“œ์™€ ์ƒ˜ํ”Œ๋Ÿฌ์˜ ๋™์ž‘์ฃผ๊ธฐ๊ฐ€ ๊ฐ™์ง€ ์•Š์€ ๋‹ค์ค‘ ์ž…์ถœ๋ ฅ(MIMO) ์ƒ˜ํ”Œ-๋ฐ์ดํ„ฐ ์‹œ์Šคํ…œ์„ ์Œ“์ธ ์‹œ์Šคํ…œ(lifted system)์œผ๋กœ ํ‘œํ˜„์Œ“์„ ๋•Œ ์ถœ๋ ฅ๋Œ€๋น„ ์ž…๋ ฅ ์—ฌ์œ ๋ถ„์ด ๋งŽ์„ ๊ฒฝ์šฐ, ์ž…๋ ฅ ๋„คํŠธ์›Œํฌ๋ฅผ ํ†ตํ•˜์—ฌ ๊ฒ€์ถœ ๋ถˆ๊ฐ€๋Šฅํ•œ ๊ณต๊ฒฉ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ์ถฉ๋ถ„์กฐ๊ฑด์„ ์ฐพ๊ณ , ์ด๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ณต๊ฒฉ์‹ ํ˜ธ๋ฅผ ์ƒ์„ฑํ•˜๋Š” ์„ค๊ณ„๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค.1 Introduction 1 1.1 Overview of Security Issues on Cyber-Physical Systems 1 1.2 Contributions and Outline of Dissertation 4 1.3 Preliminary: Characterization of detectable and undetectable attacks 8 2 Use of Generalized Hold in Sampled-data Systems to Counteract Zero Dynamics Attack 13 2.1 Zero Dynamics Attack with Normal Form 13 2.1.1 Continuous-time Linear Systems 13 2.1.2 Sampled-data Linear Systems 16 2.1.3 Simulation Result: Zero Dynamics Attack on Sampling Zeros 18 2.1.4 Existing Countermeasures Against Zero Dynamics Attack 19 2.2 Optimal Generalized Hold Function to Neutralize Zero Dynamics Attack 22 2.2.1 Shifting discrete-time zeros by generalized hold 23 2.2.2 Design of optimal generalized hold function with security guaranteed 27 2.2.3 Simulation Results: Effect of Optimal Generalized Hold 34 2.3 Illustrative Example for Closed-loop System 36 2.4 Experiment: DC/DC Converter with Electrical Circuit 39 2.4.1 Simulation Results 43 2.4.2 Experiment Results 44 2.5 Study on the Effect of Generalized Hold on Intrinsic Zeros of Nonlinear Systems under Fast Sampling 47 3 Use of Generalized Hold Feedback in Sampled-data Systems to Counteract Zero-dynamics Sensor Attack 57 3.1 Undetectable Sensor Attack and its lethality 57 3.1.1 Construction of Zero Dynamics Sensor Attack 58 3.1.2 Simulation Results: Magnetic Levitation of a Steel Ball 61 3.2 Strategy to Neutralize Zero Dynamics Sensor Attack and Relieve Performance Degradation 63 3.2.1 Employing the generalized hold feedback to neutralize zero dynamics sensor attack 64 3.2.2 Simulation Results: Effectiveness of the Generalized Hold 69 3.2.3 DLQR under Consideration of Inter-sample Behavior 71 3.2.4 Simulation Results: Effectiveness of DLQR with Continuous-time Performance Index 77 4 Masking Attack for Sampled-data System via Input Redundancy 79 4.1 Problem Formulation 79 4.2 Design of Masking Attack with Zero-stealthy and Disruptive Properties 83 4.2.1 Clustering the Time Frame 86 4.2.2 Conditions for Masking Attack Design 90 4.2.3 Off-line Construction of Attack Signal 93 4.2.4 Practical Stealthiness of Masking Attack with R \in R 97 4.3 Simulation Results 99 4.3.1 Numerical Example: R = 1 with ฮด = 0 99 4.3.2 X-38 Vehicle: R = 4 with ฮด = 0 102 4.3.3 Numerical Example: R = 0.4 with ฮด = 0.75 105 5 Conclusion of Dissertation 111 BIBLIOGRAPHY 113 ๊ตญ๋ฌธ์ดˆ๋ก 121Docto

    Bibliographical review on cyber attacks from a control oriented perspective

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    This paper presents a bibliographical review of definitions, classifications and applications concerning cyber attacks in networked control systems (NCSs) and cyber-physical systems (CPSs). This review tackles the topic from a control-oriented perspective, which is complementary to information or communication ones. After motivating the importance of developing new methods for attack detection and secure control, this review presents security objectives, attack modeling, and a characterization of considered attacks and threats presenting the detection mechanisms and remedial actions. In order to show the properties of each attack, as well as to provide some deeper insight into possible defense mechanisms, examples available in the literature are discussed. Finally, open research issues and paths are presented.Peer ReviewedPostprint (author's final draft

    Distributed Fault Detection in Formation of Multi-Agent Systems with Attack Impact Analysis

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    Autonomous Underwater Vehicles (AUVs) are capable of performing a variety of deepwater marine applications as in multiple mobile robots and cooperative robot reconnaissance. Due to the environment that AUVs operate in, fault detection and isolation as well as the formation control of AUVs are more challenging than other Multi-Agent Systems (MASs). In this thesis, two main challenges are tackled. We first investigate the formation control and fault accommodation algorithms for AUVs in presence of abnormal events such as faults and communication attacks in any of the team members. These undesirable events can prevent the entire team to achieve a safe, reliable, and efficient performance while executing underwater mission tasks. For instance, AUVs may face unexpected actuator/sensor faults and the communication between AUVs can be compromised, and consequently make the entire multi-agent system vulnerable to cyber-attacks. Moreover, a possible deception attack on network system may have a negative impact on the environment and more importantly the national security. Furthermore, there are certain requirements for speed, position or depth of the AUV team. For this reason, we propose a distributed fault detection scheme that is able to detect and isolate faults in AUVs while maintaining their formation under security constraints. The effects of faults and communication attacks with a control theoretical perspective will be studied. Another contribution of this thesis is to study a state estimation problem for a linear dynamical system in presence of a Bias Injection Attack (BIA). For this purpose, a Kalman Filter (KF) is used, where we show that the impact of an attack can be analyzed as the solution of a quadratically constrained problem for which the exact solution can be found efficiently. We also introduce a lower bound for the attack impact in terms of the number of compromised actuators and a combination of sensors and actuators. The theoretical findings are accompanied by simulation results and numerical can study examples

    Detection and Characterization of Actuator Attacks Using Kalman Filter Estimation

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    In this thesis, two discrete-time control systems subject to noise, are modeled, analyzed and estimated. These systems are then subjected to attack by false signals such as constant and ramp signals. In order to find out how and when the control systems are being attacked by the false signals, several detection algorithms are applied to the systems. This work focuses on actuator attack detection. To detect the presence of false actuator signals, a bank of Kalman filters is set up which uses adaptive estimation and conditional probability density functions for detecting the false signals. The individual Kalman filters are each tuned to satisfy a control system: one of which is the original system and the other of which is the system with a false signal. The use of the bank of Kalman filters to detect actuator attacks is tested in 4 cases; first-order system attacked by a constant or ramp signal and then a second-order system subject to the same types of attack signals. This work shows the bank of Kalman filters can successfully detect the intrusion of false signals for actuator attack by using several different detection algorithms. Simulations show that the false signal is found and detected in all cases

    A Hierarchical Architectural Framework for Securing Unmanned Aerial Systems

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    Unmanned Aerial Systems (UAS) are becoming more widely used in the new era of evolving technology; increasing performance while decreasing size, weight, and cost. A UAS equipped with a Flight Control System (FCS) that can be used to fly semi- or fully-autonomous is a prime example of a Cyber Physical and Safety Critical system. Current Cyber-Physical defenses against malicious attacks are structured around security standards for best practices involving the development of protocols and the digital software implementation. Thus far, few attempts have been made to embed security into the architecture of the system considering security as a holistic problem. Therefore, a Hierarchical, Embedded, Cyber Attack Detection (HECAD) framework is developed to provide security in a holistic manor, providing resiliency against cyber-attacks as well as introducing strategies for mitigating and dealing with component failures. Traversing the hardware/software barrier, HECAD provides detection of malicious faults at the hardware and software level; verified through the development of an FPGA implementation and tested using a UAS FCS

    Detection and Characterization of Actuator Attacks Using Kalman Filter Estimation

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    In this thesis, two discrete-time control systems subject to noise, are modeled, analyzed and estimated. These systems are then subjected to attack by false signals such as constant and ramp signals. In order to find out how and when the control systems are being attacked by the false signals, several detection algorithms are applied to the systems. This work focuses on actuator attack detection. To detect the presence of false actuator signals, a bank of Kalman filters is set up which uses adaptive estimation and conditional probability density functions for detecting the false signals. The individual Kalman filters are each tuned to satisfy a control system: one of which is the original system and the other of which is the system with a false signal. The use of the bank of Kalman filters to detect actuator attacks is tested in 4 cases; first-order system attacked by a constant or ramp signal and then a second-order system subject to the same types of attack signals. This work shows the bank of Kalman filters can successfully detect the intrusion of false signals for actuator attack by using several different detection algorithms. Simulations show that the false signal is found and detected in all cases

    State of the art of cyber-physical systems security: An automatic control perspective

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    Cyber-physical systems are integrations of computation, networking, and physical processes. Due to the tight cyber-physical coupling and to the potentially disrupting consequences of failures, security here is one of the primary concerns. Our systematic mapping study sheds light on how security is actually addressed when dealing with cyber-physical systems from an automatic control perspective. The provided map of 138 selected studies is defined empirically and is based on, for instance, application fields, various system components, related algorithms and models, attacks characteristics and defense strategies. It presents a powerful comparison framework for existing and future research on this hot topic, important for both industry and academia
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