A multi-observer based estimation framework for nonlinear systems under sensor attacks

Yang, Tianci; Murguia, Carlos; Kuijper, Margreta; Nešić, Dragan

doi:10.1016/j.automatica.2020.109043

Cited by 22 publications

(20 citation statements)

References 18 publications

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“…Remark 1 It is usually assumed in the literature, e.g., [10], [7], [12], [11], [28], [29], [31] that the set of attacked nodes is time-invariant, i.e., the attacker cannot change his mind by attacking the other nodes rather than the ones he chose initially. We relax this assumption by allowing the set of attacked sensors to be time-varying, i.e., the attacker can choose a different set of sensors to compromise at different time.…”

Section: Secure Sensor Fusion Under Sensor Attacksmentioning

confidence: 99%

“…Sensor redundancy has been proved to be crucial for estimation under attacks [27], [10], [28], [29], [14], [30]. Using redundant sensors/actuators for secure estimation and control is a commonly adopted technique, see, e.g., [27], [10], [28], [29], [11], [31], and references therein. Note that it might be costly to create redundancy, which indicates some of these estimation methods might have conservative applications; however, for security-critical systems, for instance, CAVs, this is the price to pay [6].…”

Section: Introductionmentioning

confidence: 99%

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A Secure Sensor Fusion Framework for Connected and Automated Vehicles under Sensor Attacks

Yang

2021

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By using various sensors to measure the surroundings and sharing local sensor information with the surrounding vehicles through wireless networks, connected and automated vehicles (CAVs) are expected to increase safety, efficiency, and capacity of our transportation systems. However, the increasing usage of sensors has also increased the vulnerability of CAVs to sensor faults and adversarial attacks. Anomalous sensor values resulting from malicious cyberattacks or faulty sensors may cause severe consequences or even fatalities. In this paper, we increase the resilience of CAVs to faults and attacks by using multiple sensors for measuring the same physical variable to create redundancy. We exploit this redundancy and propose a sensor fusion algorithm for providing a robust estimate of the correct sensor information with bounded errors independent of the attack signals, and for attack detection and isolation. The proposed sensor fusion framework is applicable to a large class of security-critical Cyber-Physical Systems (CPSs). To minimize the performance degradation resulting from the usage of estimation for control, we provide an H∞ controller for CACCequipped CAVs capable of stabilizing the closed-loop dynamics of each vehicle in the platoon while reducing the joint effect of estimation errors and communication channel noise on the tracking performance and string behavior of the vehicle platoon. Numerical examples are presented to illustrate the effectiveness of our methods.

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Section: Secure Sensor Fusion Under Sensor Attacksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Secure Sensor Fusion Framework for Connected and Automated Vehicles under Sensor Attacks

Yang

2021

Preprint

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“…A joint detection and estimation problem is investigated in [8] with the knowledge of some attack statistics. There are some methods for multi-observer based detector design [9][10][11]. However, the computational complexity of these methods substantially increases as the number of sensors is increasing.…”

Section: Motivations and Related Workmentioning

confidence: 99%

“…The proposed observer is able to handle more typical sensor attacks than [7,8], such as random attack, DoS attack, bias injection attack, and replay attack. The proposed detector is based on one observer, which requires less computational resources than the detectors based on multiple observers [9][10][11]. Although [20] study a wider range of attacks than this paper, we remove the requirements of graph robustness.…”

Section: Contributionsmentioning

confidence: 99%

Distributed control under compromised measurements:Resilient estimation, attack detection, and vehicle platooning

He¹,

Hashemi²,

Johansson³

2020

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We study how to design a secure observer-based distributed controller such that a group of vehicles can achieve accurate state estimates and formation control even if the measurements of a subset of vehicle sensors are compromised by a malicious attacker. We propose an architecture consisting of a resilient observer, an attack detector, and an observer-based distributed controller. The distributed detector is able to update three sets of vehicle sensors: the ones surely under attack, surely attack-free, and suspected to be under attack. The adaptive observer saturates the measurement innovation through a preset static or time-varying threshold, such that the potentially compromised measurements have limited influence on the estimation. Essential properties of the proposed architecture include: 1) The detector is faultfree, and the attacked and attack-free vehicle sensors can be identified in finite time; 2) The observer guarantees both real-time error bounds and asymptotic error bounds, with tighter bounds when more attacked or attack-free vehicle sensors are identified by the detector;3) The distributed controller ensures closed-loop stability. The effectiveness of the proposed methods is evaluated through simulations by an application to vehicle platooning.

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“…Our sensor fusion algorithm is inspired by [27], where a state estimator is constructed for general linear time-invariant (LTI) systems under sensor attacks using a bank of observers. Placing redundant sensors/actuators in systems and then exploiting sensor/actuator redundancy is a commonly adopted technique in the research area of secure estimation and control for cyber-physical systems [27]- [32]. In this manuscript, we create sensor redundancy by assuming each CAV collaborates with each other by sharing its local sensor measurements via the cloud.…”

Section: Introductionmentioning

confidence: 99%

Secure Estimation and Attack Isolation for Connected and Automated Driving in the Presence of Malicious Vehicles

Yang

2021

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Connected and Automated Vehicles (CAVs) rely on the correctness of position and other vehicle kinematics information to fulfill various driving tasks such as vehicle following, lane change, and collision avoidance. However, a malicious vehicle may send false sensor information to the other vehicles intentionally or unintentionally, which may cause traffic inconvenience or loss of human lives. Here, we take the advantage of cloud-computing and increase the resilience of CAVs to malicious vehicles by assuming each vehicle shares its local sensor information with other vehicles to create information redundancy on the cloud side. We exploit this redundancy and propose a sensor fusion algorithm for the cloud, capable of providing a robust state estimation of all vehicles in the cloud under the condition that the number of malicious information is sufficiently small. Using the proposed estimator, we provide an algorithm for isolating malicious vehicles. We use numerical examples to illustrate the effectiveness of our methods.

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A multi-observer based estimation framework for nonlinear systems under sensor attacks

Cited by 22 publications

References 18 publications

A Secure Sensor Fusion Framework for Connected and Automated Vehicles under Sensor Attacks

A Secure Sensor Fusion Framework for Connected and Automated Vehicles under Sensor Attacks

Distributed control under compromised measurements:Resilient estimation, attack detection, and vehicle platooning

Secure Estimation and Attack Isolation for Connected and Automated Driving in the Presence of Malicious Vehicles

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