Design-time Quantification of Integrity in Cyber-physical Systems

Morris, Eric Rothstein; Murguia, Carlos; Ochoa, Martín

doi:10.1145/3139337.3139347

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…. , m, enforce that D does not intersect with the new reachable set R in (14) and maximize the volume of the new minimumvolume ellipsoid E(Y −1 , m) bounding R.…”

Section: B Synthesismentioning

confidence: 99%

Constraining Attacker Capabilities Through Actuator Saturation

Kafash

Giraldo

Murguia

et al. 2018

2018 Annual American Control Conference (ACC)

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For LTI control systems, we provide mathematical tools -in terms of Linear Matrix Inequalities -for computing outer ellipsoidal bounds on the reachable sets that attacks can induce in the system when they are subject to the physical limits of the actuators. Next, for a given set of dangerous states, states that (if reached) compromise the integrity or safe operation of the system, we provide tools for designing new artificial limits on the actuators (smaller than their physical bounds) such that the new ellipsoidal bounds (and thus the new reachable sets) are as large as possible (in terms of volume) while guaranteeing that the dangerous states are not reachable. This guarantees that the new bounds cut as little as possible from the original reachable set to minimize the loss of system performance. Computer simulations using a platoon of vehicles are presented to illustrate the performance of our tools.

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“…. , m, enforce that D does not intersect with the new reachable set R in (14) and maximize the volume of the new minimumvolume ellipsoid E(Y −1 , m) bounding R.…”

Section: B Synthesismentioning

confidence: 99%

Constraining Attacker Capabilities Through Actuator Saturation

Kafash

Giraldo

Murguia

et al. 2018

2018 Annual American Control Conference (ACC)

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Systematic Classification of Attackers via Bounded Model Checking

Rothstein-Morris

Sun

Chattopadhyay

2020

Lecture Notes in Computer Science

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In this work, we study the problem of verification of systems in the presence of attackers using bounded model checking. Given a system and a set of security requirements, we present a methodology to generate and classify attackers, mapping them to the set of requirements that they can break. A naive approach suffers from the same shortcomings of any large model checking problem, i.e., memory shortage and exponential time. To cope with these shortcomings, we describe two sound heuristics based on cone-of-influence reduction and on learning, which we demonstrate empirically by applying our methodology to a set of hardware benchmark systems.

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Privacy Against State estimation: An Optimization Framework based on the Data Processing Inequality

et al. 2020

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Information about the system state is obtained through noisy sensor measurements. This data is coded and transmitted to a trusted user through an unsecured communication network. We aim at keeping the system state private; however, because the network is not secure, opponents might access sensor data, which can be used to estimate the state. To prevent this, before transmission, we randomize coded sensor data by passing it through a probabilistic mapping, and send the corrupted data to the trusted user. Making use of the data processing inequality, we cast the synthesis of the probabilistic mapping as a convex program where we minimize the mutual information (our privacy metric) between two estimators, one constructed using the randomized sensor data and the other using the actual undistorted sensor measurements, for a desired level of distortion-how different coded sensor measurements and distorted data are allowed to be.

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Design-time Quantification of Integrity in Cyber-physical Systems

Cited by 8 publications

References 39 publications

Constraining Attacker Capabilities Through Actuator Saturation

Constraining Attacker Capabilities Through Actuator Saturation

Systematic Classification of Attackers via Bounded Model Checking

Privacy Against State estimation: An Optimization Framework based on the Data Processing Inequality

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