| 1. |
- Liu, Hanxiao, et al.
(författare)
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Active Detection Against Replay Attack : A Survey on Watermark Design for Cyber-Physical Systems
- 2021
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Ingår i: Lecture Notes in Control and Information Sciences. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 145-171
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Konferensbidrag (refereegranskat)abstract
- Watermarking is a technique that embeds digital information, “watermark”, in a carrier signal to identify ownership of the signal or verify the authenticity or integrity of the carrier signal. It has been widely employed in the fields of image and signal processing. In this chapter, we survey some recent physical watermark design approaches for Cyber-Physical Systems (CPS). We focus on how to design physical watermarking to actively detect cyber-attacks, especially replay attacks, thereby securing the CPS. First, the system and the attack model are introduced. A basic physical watermarking scheme, which leverages a random noise as a watermark to detect the attack, is discussed. The optimal watermark signal is designed to achieve a trade-off between control performance and intrusion detection. Based on this scheme, several extensions are also presented, such as watermarks generated by a hidden Markov model and online data-based watermark generation. These schemes all use an additive watermarking signal. A multiplicative watermark scheme is also presented. The chapter is concluded with a discussion on some open problems on watermark design.
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| 2. |
- Liu, Hanxiao, et al.
(författare)
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An On-line Design of Physical Watermarks
- 2018
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Ingår i: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC). - : IEEE. - 9781538613955 ; , s. 440-445
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Konferensbidrag (refereegranskat)abstract
- This paper considers the problem of designing physical watermark signals to protect a control system against replay attacks. We first introduce the replay attack model, where an adversary replays the previous sensory data in order to fool the controller to believe the system is still operating normally. The physical watermarking scheme, which leverages a random control input as a watermark to detect the replay attack is introduced. The optimal watermark signal design problem is then proposed as an optimization problem, which achieves the optimal trade-off between the control performance and attack detection performance. For the system with unknown parameters, we provide a procedure to asymptotically derive the optimal watermarking signal. Numerical examples are provided to illustrate the effectiveness of the proposed strategy.
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| 3. |
- Liu, Hanxiao, et al.
(författare)
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An Online Approach to Physical Watermark Design
- 2020
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Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 65:9, s. 3895-3902
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Tidskriftsartikel (refereegranskat)abstract
- This article considers the problem of designing physical watermark signals in order to optimally detect possible replay attack in a linear time-invariant system, under the assumption that the system parameters are unknown and need to be identified online. We first provide a replay attack model, where an adversary replays the previous sensor data in order to fool the system. A physical watermarking scheme, which leverages a random input as a watermark to detect the replay attack, is then introduced. The optimal watermark signal design problem is cast as an optimization problem, which aims to achieve the optimal trade-off between control performance and intrusion detection. An online watermarking design and system identification algorithm is provided to deal with systems with unknown parameters. We prove that the proposed algorithm converges to the optimal one and characterize the almost sure convergence rate. An industrial process example is provided to illustrate the effectiveness of the proposed strategy.
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| 4. |
- Liu, Hanxiao, et al.
(författare)
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An Optimal Linear Attack Strategy on Remote State Estimation
- 2020
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Ingår i: IFAC PAPERSONLINE. - : Elsevier BV. - 2405-8963. ; , s. 3527-3532
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Konferensbidrag (refereegranskat)abstract
- This work considers the problem of designing an attack strategy on remote state estimation under the condition of strict stealthiness and 6-stealthiness of the attack. An attacker is assumed to be able to launch a linear attack to modify sensor data. A metric based on Kullback-Leibler divergence is adopted to quantify the stealthiness of the attack. We propose a generalized linear attack based on past attack signals and the latest innovation. We prove that the proposed approach can obtain an attack which can cause more estimation performance loss than linear attack strategies recently studied in the literature. The result thus provides a bound on the tradeoff between available information and attack performance, which is useful in the development of mitigation strategies. Finally, some numerical examples are given to evaluate the performance of the proposed strategy.
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| 5. |
- Liu, Hanxiao, 1995-
(författare)
-
Analysis, Detection, and Mitigation of Attacks in Cyber-physical Systems
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cyber-Physical Systems (CPS) offer close integration among computational elements, communication networks, and physical processes. Such systems play an increasingly important role in a large variety of fields, such as manufacturing, health care, environment, transportation, defence, and so on. Due to the wide applications and critical functions of CPS, increasing importance has been attached to their security. In this thesis, we focus on the security of CPS by investigating vulnerability under cyber-attacks, providing detection mechanisms, and developing feasible countermeasures against cyber-attacks.The first contribution of this thesis is to analyze the performance of remote state estimation under linear attacks. A linear time-invariant system equipped with a smart sensor is studied. The adversary aims to maximize the state estimation error covariance while staying stealthy. The maximal performance degradation that an adversary can achieve with any linear first-order false data injection attack under strict stealthiness for vector systems and $\epsilon$-stealthiness for scalar systems is characterized. We also provide an explicit attack strategy that achieves this bound and compare it with strategies previously proposed in the literature. The second problem of this thesis is about the detection of replay attacks. We aim to design physical watermark signals and corresponding detector to protect a control system against replay attacks. For a scenario where the system parameters are available to the operator, a physical watermarking scheme to detect the replay attack is introduced. The optimal watermark signal design problem is formulated as an optimization problem, and the optimal watermark signal and detector are derived. Subsequently, for systems with unknown parameters, we provide an on-line learning mechanism to asymptotically derive the optimal watermarking signal and corresponding detector.The third problem under investigation is about the detection of false-data injection attacks when the attacker injects malicious data to flip the distribution of the manipulated sensor measurements. The detector decides to continue taking observations or to stop based on the received signals, and the goal is to have the flip attack detected as fast as possible while trying to avoid terminating the measurements when no attack is present. The detection problem is modeled as a partially observable Markov decision process (POMDP) by assuming an attack probability, with the dynamics of the hidden states of the POMDP characterized by a stochastic shortest path (SSP) problem. The optimal policy of the SSP solely depends on the transition costs and is independent of the assumed attack probability. By using a fixed-length window and suitable feature function of the measurements, a Markov decision process (MDP) is used to approximate the POMDP. The optimal solution of the MDP is obtained by reinforcement learning. The fourth contribution of this thesis is to develop a sensor scheduler for remote state estimation under integrity attacks. We seek a trade-off between the energy consumption of communications and accuracy of state estimation when the acknowledgment (ACK) information, sent by the remote estimator to the local sensor, is compromised. The sensor scheduling problem is formulated as an infinite horizon discounted optimal control problem with infinite states. We first analyze the underlying MDP and show that the optimal schedule without ACK attack is of threshold type. Thus, we can simplify the problem by replacing the original state space with a finite state space. For the simplified MDP, when ACK is under attack, the problem is modelled as a POMDP. We analyze the induced MDP that uses a belief vector as its state for the POMDP. The properties of the exact optimal solution are studied via contractive models and it is shown that the threshold solution for the POMDP cannot be readily obtained. A suboptimal solution is provided instead via a rollout approach based on reinforcement learning. We present two variants of rollout and provide corresponding performance bounds.
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| 6. |
- Liu, Hanxiao, et al.
(författare)
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How vulnerable is innovation-based remote state estimation : Fundamental limits under linear attacks
- 2022
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Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 136, s. 110079-
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with the problem of how secure the innovation-based remote state estimation can be under linear attacks. A linear time-invariant system equipped with a smart sensor is studied. A metric based on Kullback–Leibler divergence is adopted to characterize the stealthiness of the attack. The adversary aims to maximize the state estimation error covariance while stay stealthy. The maximal performance degradations that an adversary can achieve with any linear first-order false-data injection attack under strict stealthiness for vector systems and ε-stealthiness for scalar systems are characterized. We also provide an explicit attack strategy that achieves this bound and compare this attack strategy with strategies previously proposed in the literature. Finally, some numerical examples are given to illustrate the results.
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| 7. |
- Liu, Hanxiao, et al.
(författare)
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Reinforcement Learning Based Approach for Flip Attack Detection
- 2020
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Ingår i: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 3212-3217
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the detection problem of flip attacks to sensor network systems where the attacker flips the distribution of manipulated sensor measurements of a binary state. The detector decides to continue taking observations or to stop based on the sensor measurements, and the goal is to have the flip attack recognized as fast as possible while trying to avoid terminating the measurements when no attack is present. The detection problem can be modeled as a partially observable Markov decision process (POMDP) by assuming an attack probability, with the dynamics of the hidden states of the POMDP characterized by a stochastic shortest path (SSP) problem. The optimal policy of the SSP solely depends on the transition costs and is independent of the assumed attack possibility. By using a fixed-length window and suitable feature function of the measurements, a Markov decision process (MDP) is used to approximate the behavior of the POMDP. The optimal solution of the approximated MDP can then be solved by any standard reinforcement learning methods. Numerical evaluations demonstrates the effectiveness of the method.
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| 8. |
- Liu, Hanxiao, et al.
(författare)
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Rollout approach to sensor scheduling for remote state estimation under integrity attack
- 2022
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Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 144
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Tidskriftsartikel (refereegranskat)abstract
- We consider the sensor scheduling problem for remote state estimation under integrity attacks. We seek to optimize a trade-off between the energy consumption of communications and the state estimation error covariance when the acknowledgment (ACK) information, sent by the remote estimator to the local sensor, is compromised. The sensor scheduling problem is formulated as an infinite horizon discounted optimal control problem with infinite states. We first analyze the underlying Markov decision process (MDP) and show that the optimal scheduling without ACK attack is of the threshold type. Thus, we can simplify the problem by replacing the original state space with a finite state space. For the simplified MDP, when the ACK is under attack, the problem is modeled as a partially observable Markov decision process (POMDP). We analyze the induced MDP that uses a belief vector as its state for the POMDP. We investigate the properties of the exact optimal solution via contractive models and show that the threshold type of solution for the POMDP cannot be readily obtained. A suboptimal solution is then obtained via a rollout approach, which is a prominent class of reinforcement learning (RL) methods based on approximation in value space. We present two variants of rollout and provide performance bounds of those variants. Finally, numerical examples are used to demonstrate the effectiveness of the proposed rollout methods by comparing them with a finite history window approach that is widely used in RL for POMDP.
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