Cyber-physical system

About: Cyber-physical system is a research topic. Over the lifetime, 11096 publications have been published within this topic receiving 162489 citations. The topic is also known as: CPS.

The AI-Based Cyber Threat Landscape: A Survey

Abstract: Recent advancements in artificial intelligence (AI) technologies have induced tremendous growth in innovation and automation. Although these AI technologies offer significant benefits, they can be used maliciously. Highly targeted and evasive attacks in benign carrier applications, such as DeepLocker, have demonstrated the intentional use of AI for harmful purposes. Threat actors are constantly changing and improving their attack strategy with particular emphasis on the application of AI-driven techniques in the attack process, called AI-based cyber attack, which can be used in conjunction with conventional attack techniques to cause greater damage. Despite several studies on AI and security, researchers have not summarized AI-based cyber attacks enough to be able to understand the adversary’s actions and to develop proper defenses against such attacks. This study aims to explore existing studies of AI-based cyber attacks and to map them onto a proposed framework, providing insight into new threats. Our framework includes the classification of several aspects of malicious uses of AI during the cyber attack life cycle and provides a basis for their detection to predict future threats. We also explain how to apply this framework to analyze AI-based cyber attacks in a hypothetical scenario of a critical smart grid infrastructure.

Compositional Falsification of Cyber-Physical Systems with Machine Learning Components.

Abstract: Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks.

A Framework for Modeling Cyber-Physical Switching Attacks in Smart Grid

Abstract: Security issues in cyber-physical systems are of paramount importance due to the often safety-critical nature of its associated applications. A first step in understanding how to protect such systems requires an understanding of emergent weaknesses, in part, due to the cyber-physical coupling. In this paper, we present a framework that models a class of cyber-physical switching vulnerabilities in smart grid systems. Variable structure system theory is employed to effectively characterize the cyber-physical interaction of the smart grid and demonstrate how existence of the switching vulnerability is dependent on the local structure of the power grid. We identify and demonstrate how through successful cyber intrusion and local knowledge of the grid an opponent can compute and apply a coordinated switching sequence to a circuit breaker to disrupt operation within a short interval of time. We illustrate the utility of the attack approach empirically on the Western Electricity Coordinating Council three-machine, nine-bus system under both model error and partial state information.

Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System

Abstract: Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85% of them from the data logs generated at runtime.

S3A: secure system simplex architecture for enhanced security and robustness of cyber-physical systems

Abstract: The recently discovered 'W32.Stuxnet' worm has drastically changed the perception that systems managing critical infrastructure are invulnerable to software security attacks. Here we present an architecture that enhances the security of safety-critical cyber-physical systems despite the presence of such malware. Our architecture uses the property that control systems have deterministic real-time) execution behavior to detect an intrusion within 0.6 μs while still guaranteeing the safety of the plant. We also show that even if an attacker is successful (or gains access to the operating system's administrative privileges), the overall state of the physical system still remains safe.