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  • 1.
    Li, Zishuo
    et al.
    Tsinghua University.
    Nguyen, Anh Tung
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Teixeira, André
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Mo, Yilin
    Tsinghua University.
    Johansson, Karl H
    KTH Royal Institute of Technology.
    Secure State Estimation with Asynchronous Measurements against Malicious Measurement-data and Time-stamp Manipulation2023In: 2023 62nd IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 7073-7080Conference paper (Refereed)
    Abstract [en]

    This paper proposes a secure state estimation scheme with non-periodic asynchronous measurements for linear continuous-time systems under false data attacks on the measurement transmit channel. After sampling the output of the system, a sensor transmits the measurement information in a triple composed of sensor index, time-stamp, and measurement value to the fusion center via vulnerable communication channels. The malicious attacker can corrupt a subset of the sensors through (i) manipulating the time-stamp and measurement value; (ii) blocking transmitted measurement triples; or (iii) injecting fake measurement triples. To deal with such attacks, we propose the design of local estimators based on observability space decomposition, where each local estimator updates the local state and sends it to the fusion center after sampling a measurement. Whenever there is a local update, the fusion center combines all the local states and generates a secure state estimate by adopting the median operator. We prove that local estimators of benign sensors are unbiased with stable covariance. Moreover, the fused central estimation error has bounded expectation and covariance against at most p corrupted sensors as long as the system is 2p-sparse observable. The efficacy of the proposed scheme is demonstrated through an application on a benchmark example of the IEEE 14-bus system.

  • 2.
    Nguyen, Anh Tung
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Security Allocation in Networked Control Systems2023Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Sustained use of critical infrastructure, such as electrical power and water distribution networks, requires efficient management and control. Facilitated by the advancements in computational devices and non-proprietary communication technology, such as the Internet, the efficient operation of critical infrastructure relies on network decomposition into interconnected subsystems, thus forming networked control systems. However, the use of public and pervasive communication channels leaves these systems vulnerable to cyber attacks. Consequently, the critical infrastructure is put at risk of suffering operation disruption and even physical damage that would inflict financial costs as well as pose a hazard to human health. Therefore, security is crucial to the sustained efficient operation of critical infrastructure. This thesis develops a framework for evaluating and improving the security of networked control systems in the face of cyberattacks. The considered security problem involves two strategic agents, namely a malicious adversary and a defender, pursuing their specific and conflicting goals. The defender aims to efficiently allocate defense resources with the purpose of detecting malicious activities. Meanwhile, the malicious adversary simultaneously conducts cyber attacks and remains stealthy to the defender. We tackle the security problem by proposing a game-theoretic framework and characterizing its main components: the payoff function, the action space, and the available information for each agent. Especially, the payoff function is characterized based on the output-to-output gain security metric that fully explores the worst-case attack impact. Then, we investigate the properties of the game and how to efficiently compute its equilibrium. Given the combinatorial nature of the defender’s actions, one important challenge is to alleviate the computational burden. To overcome this challenge, the thesis contributes several system- and graph-theoretic conditions that enable the defender to shrink the action space, efficiently allocating the defense resources. The effectiveness of the proposed framework is validated through numerical examples. 

    List of papers
    1. A Single-Adversary-Single-Detector Zero-Sum Game in Networked Control Systems
    Open this publication in new window or tab >>A Single-Adversary-Single-Detector Zero-Sum Game in Networked Control Systems
    2022 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 55, no 13, p. 49-54Article in journal (Refereed) Published
    Abstract [en]

    This paper proposes a game-theoretic approach to address the problem of optimal sensor placement for detecting cyber-attacks in networked control systems. The problem is formulated as a zero-sum game with two players, namely a malicious adversary and a detector. Given a protected target vertex, the detector places a sensor at a single vertex to monitor the system and detect the presence of the adversary. On the other hand, the adversary selects a single vertex through which to conduct a cyber-attack that maximally disrupts the target vertex while remaining undetected by the detector. As our first contribution, for a given pair of attack and monitor vertices and a known target vertex, the game payoff function is defined as the output-to-output gain of the respective system. Then, the paper characterizes the set of feasible actions by the detector that ensures bounded values of the game payoff. Finally, an algebraic sufficient condition is proposed to examine whether a given vertex belongs to the set of feasible monitor vertices. The optimal sensor placement is then determined by computing the mixed-strategy Nash equilibrium of the zero-sum game through linear programming. The approach is illustrated via a numerical example of a 10-vertex networked control system with a given target vertex.

    Place, publisher, year, edition, pages
    Elsevier, 2022
    Keywords
    Cyber-physical security, networked control systems, game theory
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-485955 (URN)10.1016/j.ifacol.2022.07.234 (DOI)000852734000009 ()
    Conference
    9th IFAC Conference on Networked Systems (NECSYS), JUL 05-07, 2022, Zurich, Switzerland
    Funder
    Swedish Research Council, 2018-04396Swedish Research Council, 2021-06316Swedish Foundation for Strategic Research
    Available from: 2022-09-30 Created: 2022-09-30 Last updated: 2023-12-28Bibliographically approved
    2. A Zero-Sum Game Framework for Optimal Sensor Placement in Uncertain Networked Control Systems under Cyber-Attacks
    Open this publication in new window or tab >>A Zero-Sum Game Framework for Optimal Sensor Placement in Uncertain Networked Control Systems under Cyber-Attacks
    2022 (English)In: 2022 IEEE 61st Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2022, , p. 8p. 6126-6133Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper proposes a game-theoretic approach to address the problem of optimal sensor placement against an adversary in uncertain networked control systems. The problem is formulated as a zero-sum game with two players, namely a malicious adversary and a detector. Given a protected performance vertex, we consider a detector, with uncertain system knowledge, that selects another vertex on which to place a sensor and monitors its output with the aim of detecting the presence of the adversary. On the other hand, the adversary, also with uncertain system knowledge, chooses a single vertex and conducts a cyber-attack on its input. The purpose of the adversary is to drive the attack vertex as to maximally disrupt the protected performance vertex while remaining undetected by the detector. As our first contribution, the game payoff of the above-defined zero-sum game is formulated in terms of the Value-at-Risk of the adversary’s impact. However, this game payoff corresponds to an intractable optimization problem. To tackle the problem, we adopt the scenario approach to approximately compute the game payoff. Then, the optimal monitor selection is determined by analyzing the equilibrium of the zero-sum game. The proposed approach is illustrated via a numerical example of a 10-vertex networked control system.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2022. p. 8
    Series
    Proceedings of the IEEE Conference on Decision & Control, ISSN 0743-1546, E-ISSN 2576-2370
    Keywords
    Systems and Control (eess.SY), FOS: Electrical engineering, electronic engineering, information engineering
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-489467 (URN)10.1109/CDC51059.2022.9992468 (DOI)000948128105028 ()978-1-6654-6762-9 (ISBN)978-1-6654-6761-2 (ISBN)978-1-6654-6760-5 (ISBN)
    Conference
    2022 IEEE 61st Conference on Decision and Control (CDC), 6-9 December 2022, Cancun, Mexico
    Funder
    Swedish Research Council, 2018-04396Swedish Research Council, 2021-06316Swedish Foundation for Strategic Research
    Available from: 2022-11-30 Created: 2022-11-30 Last updated: 2023-12-28Bibliographically approved
    3. Optimal Detector Placement in Networked Control Systems under Cyber-attacks with Applications to Power Networks
    Open this publication in new window or tab >>Optimal Detector Placement in Networked Control Systems under Cyber-attacks with Applications to Power Networks
    2023 (English)Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper proposes a game-theoretic method to address the problem of optimal detector placement in a networked control system under cyber-attacks. The networked control system is composed of interconnected agents where each agent is regulated by its local controller over unprotected communication, which leaves the system vulnerable to malicious cyber-attacks. To guarantee a given local performance, the defender optimally selects a single agent on which to place a detector at its local controller with the purpose of detecting cyber-attacks. On the other hand, an adversary optimally chooses a single agent on which to conduct a cyber-attack on its input with the aim of maximally worsening the local performance while remaining stealthy to the defender. First, we present a necessary and sufficient condition to ensure that the maximal attack impact on the local performance is bounded, which restricts the possible actions of the defender to a subset of available agents. Then, by considering the maximal attack impact on the local performance as a game payoff, we cast the problem of finding optimal actions of the defender and the adversary as a zero-sum game. Finally, with the possible action sets of the defender and the adversary, an algorithm is devoted to determining the Nash equilibria of the zero-sum game that yield the optimal detector placement. The proposed method is illustrated on an IEEE benchmark for power systems.

    Place, publisher, year, edition, pages
    Elsevier, 2023
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-518875 (URN)10.1016/j.ifacol.2023.10.1896 (DOI)
    Conference
    22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023
    Available from: 2023-12-27 Created: 2023-12-27 Last updated: 2024-01-10Bibliographically approved
    4. Security Allocation in Networked Control Systems under Stealthy Attacks
    Open this publication in new window or tab >>Security Allocation in Networked Control Systems under Stealthy Attacks
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    This paper considers the problem of security allocation in a networked control system under stealthy attacks in which the system is comprised of interconnected subsystems represented by vertices. A malicious adversary selects a single vertex on which to conduct a stealthy data injection attack to maximally disrupt the local performance while remaining undetected. On the other hand, a defender selects several vertices on which to allocate defense resources against the adversary. First, the objectives of the adversary and the defender with uncertain targets are formulated in probabilistic ways, resulting in an expected worst-case impact of stealthy attacks. Next, we provide a graph-theoretic necessary and sufficient condition under which the cost for the defender and the expected worst-case impact of stealthy attacks are bounded. This condition enables the defender to restrict the admissible actions to a subset of available vertex sets. Then, we cast the problem of security allocation in a Stackelberg game-theoretic framework. Finally, the contribution of this paper is highlighted by utilizing the proposed admissible actions of the defender in the context of large-scale networks. A numerical example of a 50-vertex networked control system is presented to validate the obtained results.

    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-522013 (URN)
    Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-04-15Bibliographically approved
    Download full text (pdf)
    fulltext
  • 3.
    Nguyen, Anh Tung
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Anand, Sribalaji C.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.
    Teixeira, André M. H.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A Zero-Sum Game Framework for Optimal Sensor Placement in Uncertain Networked Control Systems under Cyber-Attacks2022In: 2022 IEEE 61st Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2022, , p. 8p. 6126-6133Conference paper (Refereed)
    Abstract [en]

    This paper proposes a game-theoretic approach to address the problem of optimal sensor placement against an adversary in uncertain networked control systems. The problem is formulated as a zero-sum game with two players, namely a malicious adversary and a detector. Given a protected performance vertex, we consider a detector, with uncertain system knowledge, that selects another vertex on which to place a sensor and monitors its output with the aim of detecting the presence of the adversary. On the other hand, the adversary, also with uncertain system knowledge, chooses a single vertex and conducts a cyber-attack on its input. The purpose of the adversary is to drive the attack vertex as to maximally disrupt the protected performance vertex while remaining undetected by the detector. As our first contribution, the game payoff of the above-defined zero-sum game is formulated in terms of the Value-at-Risk of the adversary’s impact. However, this game payoff corresponds to an intractable optimization problem. To tackle the problem, we adopt the scenario approach to approximately compute the game payoff. Then, the optimal monitor selection is determined by analyzing the equilibrium of the zero-sum game. The proposed approach is illustrated via a numerical example of a 10-vertex networked control system.

    The full text will be freely available from 2025-01-10 11:31
  • 4.
    Nguyen, Anh Tung
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Coimbatore Anand, Sribalaji
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.
    Teixeira, André
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Optimal Detector Placement in Networked Control Systems under Cyber-attacks with Applications to Power Networks2023Conference paper (Refereed)
    Abstract [en]

    This paper proposes a game-theoretic method to address the problem of optimal detector placement in a networked control system under cyber-attacks. The networked control system is composed of interconnected agents where each agent is regulated by its local controller over unprotected communication, which leaves the system vulnerable to malicious cyber-attacks. To guarantee a given local performance, the defender optimally selects a single agent on which to place a detector at its local controller with the purpose of detecting cyber-attacks. On the other hand, an adversary optimally chooses a single agent on which to conduct a cyber-attack on its input with the aim of maximally worsening the local performance while remaining stealthy to the defender. First, we present a necessary and sufficient condition to ensure that the maximal attack impact on the local performance is bounded, which restricts the possible actions of the defender to a subset of available agents. Then, by considering the maximal attack impact on the local performance as a game payoff, we cast the problem of finding optimal actions of the defender and the adversary as a zero-sum game. Finally, with the possible action sets of the defender and the adversary, an algorithm is devoted to determining the Nash equilibria of the zero-sum game that yield the optimal detector placement. The proposed method is illustrated on an IEEE benchmark for power systems.

  • 5.
    Nguyen, Anh Tung
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Teixeira, André M. H.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Security Allocation in Networked Control Systems under Stealthy AttacksManuscript (preprint) (Other academic)
    Abstract [en]

    This paper considers the problem of security allocation in a networked control system under stealthy attacks in which the system is comprised of interconnected subsystems represented by vertices. A malicious adversary selects a single vertex on which to conduct a stealthy data injection attack to maximally disrupt the local performance while remaining undetected. On the other hand, a defender selects several vertices on which to allocate defense resources against the adversary. First, the objectives of the adversary and the defender with uncertain targets are formulated in probabilistic ways, resulting in an expected worst-case impact of stealthy attacks. Next, we provide a graph-theoretic necessary and sufficient condition under which the cost for the defender and the expected worst-case impact of stealthy attacks are bounded. This condition enables the defender to restrict the admissible actions to a subset of available vertex sets. Then, we cast the problem of security allocation in a Stackelberg game-theoretic framework. Finally, the contribution of this paper is highlighted by utilizing the proposed admissible actions of the defender in the context of large-scale networks. A numerical example of a 50-vertex networked control system is presented to validate the obtained results.

  • 6.
    Nguyen, Anh Tung
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Teixeira, André
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    A Single-Adversary-Single-Detector Zero-Sum Game in Networked Control Systems2022In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 55, no 13, p. 49-54Article in journal (Refereed)
    Abstract [en]

    This paper proposes a game-theoretic approach to address the problem of optimal sensor placement for detecting cyber-attacks in networked control systems. The problem is formulated as a zero-sum game with two players, namely a malicious adversary and a detector. Given a protected target vertex, the detector places a sensor at a single vertex to monitor the system and detect the presence of the adversary. On the other hand, the adversary selects a single vertex through which to conduct a cyber-attack that maximally disrupts the target vertex while remaining undetected by the detector. As our first contribution, for a given pair of attack and monitor vertices and a known target vertex, the game payoff function is defined as the output-to-output gain of the respective system. Then, the paper characterizes the set of feasible actions by the detector that ensures bounded values of the game payoff. Finally, an algebraic sufficient condition is proposed to examine whether a given vertex belongs to the set of feasible monitor vertices. The optimal sensor placement is then determined by computing the mixed-strategy Nash equilibrium of the zero-sum game through linear programming. The approach is illustrated via a numerical example of a 10-vertex networked control system with a given target vertex.

    Download full text (pdf)
    fulltext
  • 7.
    Nguyen, Binh
    et al.
    Texas A&M Univ Corpus Christi, Coll Engn, Corpus Christi, TX 78412 USA..
    Nghiem, Truong
    No Arizona Univ, Sch Informat Comp & Cyber Syst, Flagstaff, AZ 86011 USA..
    Nguyen, Linh
    Federat Univ Australia, Inst Innovat Sci & Sustainabil, Churchill, Vic 3842, Australia..
    Nguyen, Anh Tung
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Nguyen, Thang
    Texas A&M Univ Corpus Christi, Coll Engn, Corpus Christi, TX 78412 USA..
    Real-time distributed trajectory planning for mobile robots2023In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 56, no 2, p. 2152-2157Article in journal (Refereed)
    Abstract [en]

    Efficiently planning trajectories for nonholonomic mobile robots in formation tracking is a fundamental yet challenging problem. Nonholonomic constraints, complexity in collision avoidance, and limited computing resources prevent the robots from being practically deployed in realistic applications. This paper addresses these difficulties by modeling each mobile platform as a nonholonomic motion and formulating trajectory planning as an optimization problem using model predictive control (MPC). That is, the optimization problem is subject to both nonholonomic motions and collision avoidance. To reduce computing costs in real time, the nonholonomic constraints are convexified by finding the closest nominal points to the nonholonomic motion, which are then incorporated into a convex optimization problem. Additionally, the predicted values from the previous MPC step are utilized to form linear avoidance conditions for the next step, preventing collisions among robots. The formulated optimization problem is solved by the alternating direction method of multiplier (ADMM) in a distributed manner, which makes the proposed trajectory planning method scalable. More importantly, the convergence of the proposed planning algorithm is theoretically proved while its effectiveness is validated in a synthetic environment.

    Download full text (pdf)
    fulltext
  • 8.
    Nguyen, Binh
    et al.
    Texas A&M Univ Corpus Christi, Dept Engn, Corpus Christi, TX 78412 USA..
    Nghiem, Truong
    No Arizona Univ, Sch Informat Comp & Cyber Syst, Flagstaff, AZ 86011 USA..
    Nguyen, Linh
    Federat Univ Australia, Sch Engn Informat Technol & Phys Sci, Churchill, Vic 3842, Australia..
    Nguyen, Tung
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    La, Hung
    Univ Nevada, Dept Comp Sci & Engn, Adv Robot & Automat ARA Lab, Reno, NV 89557 USA..
    Sookhak, Mehdi
    Texas A&M Univ Corpus Christi, Dept Comp Sci, Corpus Christi, TX 78412 USA..
    Nguyen, Thang
    Texas A&M Univ Corpus Christi, Dept Engn, Corpus Christi, TX 78412 USA..
    Distributed formation trajectory planning for multi-vehicle systems2023In: 2023 American Control Conference (ACC), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 1325-1330Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of distributed formation trajectory planning for multi-vehicle systems with collision avoidance among vehicles. Unlike some previous distributed formation trajectory planning methods, our proposed approach offers great flexibility in handling computational tasks for each vehicle when the global formation of all the vehicles changes. It affords the system the ability to adapt to the computational capabilities of the vehicles. Furthermore, global formation constraints can be handled at any selected vehicles. Thus, any formation change can be effectively updated without recomputing all local formations at all the vehicles. To guarantee the above features, we first formulate a dynamic consensus-based optimization problem to achieve desired formations while guaranteeing collision avoidance among vehicles. Then, the optimization problem is effectively solved by ADMM-based or alternating projection-based algorithms, which are also presented. Theoretical analysis is provided not only to ensure the convergence of our method but also to show that the proposed algorithm can surely be implemented in a fully distributed manner. The effectiveness of the proposed method is illustrated by a numerical example of a 9-vehicle system.

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