Analysis Of Cyber-physical Systems Research Articles

A comparative risk analysis on CyberShip system with STPA-Sec, STRIDE and CORAS

The widespread use of software-intensive cyber systems in critical infrastructures such as ships (CyberShips) has brought huge benefits, yet it has also opened new avenues for cyber attacks to potentially disrupt operations. Cyber risk assessment plays a vital role in identifying cyber threats and vulnerabilities that can be exploited to compromise cyber systems. Understanding the nature of cyber threats and their potential risks and impact is essential to improve the security and resilience of cyber systems, and to build systems that are secure by design and better prepared to detect and mitigate cyber attacks. A number of methodologies have been proposed to carry out these analyses. This paper evaluates and compares the application of three risk assessment methodologies: system theoretic process analysis (STPA-Sec), STRIDE and CORAS for identifying threats and vulnerabilities in a CyberShip system. We specifically selected these three methodologies because they identify threats not only at the component level, but also threats or hazards caused due to the interaction between components, resulting in sets of threats identified with each methodology and relevant differences. Moreover, STPA-Sec, which is a variant of the STPA, is widely used for safety and security analysis of cyber physical systems (CPS); CORAS offers a framework to perform cyber risk assessment in a top-down approach that aligns with STPA-Sec; and STRIDE (Spoofing, Tampering, Repudiation,Information disclosure, Denial of Service, Elevation of Privilege) considers threat at the component level as well as during the interaction that is similar to STPA-Sec. As a result of this analysis, this paper highlights the pros and cons of these methodologies, illustrates areas of special applicability, and suggests that their complementary use as threats identified through STRIDE can be used as an input to CORAS and STPA-Sec to make these methods more structured.

A review on security analysis of cyber physical systems using Machine learning

The concept of Cyber Physical System (CPS) is widely used in different industries across the globe. In fact, it is the holistic approach towards dealing with cyber space and physical environments that do have inter-dependencies. In the existing systems, there was a separate approach for security of the two worlds (cyber and physical). However, it does not provide necessary security when security is employed independently. The research in this paper identifies the need for integrated security for CPS. Besides it throws light into different security challenges associated with CPS and the countermeasures that existed based on machine learning and deep learning techniques that come under Artificial Intelligence (AI) and data science. From the review of literature, it is understood that data science perspective is suitable for protecting CPS with required adaptive strategy. This paper provides several useful insights related to security analysis of CPS using machine learning. It paves way for further investigation and realize a comprehensive security framework to protect CPS from internal and external cyber-attacks.

Vulnerability analysis of cyber physical systems under the false alarm cyber attacks

More and more infrastructure networks have evolved into cyber physical systems (CPS). Among the previous work, CPS has developed a series of emergency mechanisms to deal with physical failures or attacks. However, the occurrence of cyber attacks, can still severely affect the normal operation of CPS. Thus, the goal of this paper is studying and analyzing the vulnerability of CPS under the cyber attacks. Taking the smart grid as a typical CPS, we consider a specific attack scenario—false alarm attack (FAA), and mathematically propose the FAA model. The attackers can inject hidden vector to make the control center mistakenly believe that links are overloaded, which further results in unnecessary power loss. Based on the FAA model, we further analyze the vulnerability of CPS under different network structures and redundant parameters. Finally, we propose a hybrid indicator, which takes the topological and electrical characteristics of links into consideration, to identify the vulnerable links in smart grid. The simulation results show that the hybrid indicator can effectively identify the vulnerable links, and has a strong correlation with the vulnerability of smart grid. Our work can provide new insights into the construction of robust CPS.

Scenic: a language for scenario specification and data generation

We propose a new probabilistic programming language for the design and analysis of cyber-physical systems, especially those based on machine learning. We consider several problems arising in the design process, including training a system to be robust to rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs, then sampling these to generate specialized training and test data. More generally, such languages can be used to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems such as autonomous cars and robots, whose environment at any point in time is a scene, a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic’s domain-specific syntax. Finally, we apply Scenic in multiple case studies for training, testing, and debugging neural networks for perception both as standalone components and within the context of a full cyber-physical system.

Categorical Semantics of Cyber-Physical Systems Theory

Cyber-physical systems require the construction and management of various models to assure their correct, safe, and secure operation. These various models are necessary because of the coupled physical and computational dynamics present in cyber-physical systems. However, to date the different model views of cyber-physical systems are largely related informally, which raises issues with the degree of formal consistency between those various models of requirements, system behavior, and system architecture. We present a category-theoretic framework to make different types of composition explicit in the modeling and analysis of cyber-physical systems, which could assist in verifying the system as a whole. This compositional framework for cyber-physical systems gives rise to unified system models, where system behavior is hierarchically decomposed and related to a system architecture using the systems-as-algebras paradigm. As part of this paradigm, we show that an algebra of (safety) contracts generalizes over the state of the art, providing more uniform mathematical tools for constraining the behavior over a richer set of composite cyber-physical system models, which has the potential of minimizing or eliminating hazardous behavior.

On Approximate Opacity of Cyber-Physical Systems

Opacity is an important information-flow security property in the analysis of cyber-physical systems. It captures the plausible deniability of the system's secret behavior in the presence of an intruder that may access the information flow. Existing works on opacity only consider nonmetric systems by assuming that the intruder can always distinguish between two different outputs precisely. In this article, we extend the concept of opacity to systems whose output sets are equipped with metrics. Such systems are widely used in the modeling of many real-world systems whose measurements are physical signals. A new concept called approximate opacity is proposed in order to quantitatively evaluate the security guarantee level with respect to the measurement precision of the intruder. Then, we propose a new simulation-type relation, called approximate opacity-preserving simulation relation, which characterizes how close two systems are in terms of the satisfaction of approximate opacity. This allows us to verify approximate opacity for large-scale, or even infinite, systems using their abstractions. We also discuss how to construct approximate opacity-preserving symbolic models for a class of discrete-time control systems. Our results extend the definitions and analysis techniques for opacity from nonmetric systems to metric systems.

Leveraging Biologically Inspired Models for Cyber–Physical Systems Analysis

Cyber–physical systems (CPS) are systems composed of distributed sensors, physical actuators, and controlling computers that are interconnected through a computer network. Notable examples include: electric utility “smart grids” that can sense and optimize power distribution, transportation systems, and healthcare and medical systems. As CPS complexity increases, system-level trade studies become more challenging due to the combined interaction of the computing, network communications, and physical sensor and actuator elements. Although fundamentally different, complex CPS and biological systems share common attributes that suggest the use of similar modeling approaches. This research investigated if CPS multiobjective optimization methods using mathematical models of a CPS that incorporate biologically inspired CPS system performance objective functions are better than those methods that do not use biologically inspired objective functions. The research methodology employed computer modeling and simulation. A multiobjective optimization study, using a commercially available genetic algorithm-based optimizer, was conducted on four slightly different versions of a dynamic model of a simple distributed robotic sensor system to assess the effect of incorporating a biological mathematical model on the quality of the Pareto fronts generated by the optimizer. An improvement in Pareto front quality was found after incorporating a biological mathematical model.

Dependability analysis of cyber physical systems

As cyber physical system (CPS) is often used in safety critical areas, dependability of the system is an important issue that needs to be analysed. Any failure on the components of the CPS could result in a degradation of the physical state, which then causes major harm to life and/or property. Since the concept of dependence leads to that of trust, the subsystems of the CPS should be dependable to each other to deliver the requested services as specified without failing during its operation. In this study, the authors apply Markov chain to model and analyse the component dependability of CPSs and propose the recovery techniques to guarantee a high level of dependability so as to take care of assuring the continuity of system operation.

Testing Cyber-Physical Systems through Bayesian Optimization

Many problems in the design and analysis of cyber-physical systems (CPS) reduce to the following optimization problem: given a CPS which transforms continuous-time input traces in R m to continuous-time output traces in R n and a cost function over output traces, find an input trace which minimizes the cost. Cyber-physical systems are typically so complex that solving the optimization problem analytically by examining the system dynamics is not feasible. We consider a black-box approach, where the optimization is performed by testing the input-output behaviour of the CPS. We provide a unified, tool-supported methodology for CPS testing and optimization. Our tool is the first CPS testing tool that supports Bayesian optimization. It is also the first to employ fully automated dimensionality reduction techniques. We demonstrate the potential of our tool by running experiments on multiple industrial case studies. We compare the effectiveness of Bayesian optimization to state-of-the-art testing techniques based on CMA-ES and Simulated Annealing.

Guest Editorial: Special Issue on Rigorous Modeling and Analysis of Cyber-Physical Systems

The three papers in this special issue focus on rigorous modeling and analysis of cyber-physical systems.

An FPGA-Based Plant-on-Chip Platform for Cyber-Physical System Analysis

Digital control systems are traditionally designed independent of their implementation platform, assuming constant sensor sampling rates and processor response times. Applications are deployed to processors that are shared amongst control and noncontrol tasks, to maximize resource utilization. This potentially overlooks that computing mechanisms meant for improving average CPU usage, such as cache, interrupts, and task management through schedulers, contribute to nondeterministic interference between tasks. This response time jitter can result in reduced system stability, motivating further study by both the controls and computing communities to maximize CPU utilization, while maintaining physical system stability needs. In this letter, we describe an field-programmable gate array (FPGA)-based embedded software platform coupled with a hardware plant emulator (as opposed to purely software-based simulations or hardware-in-the-loop setups) that forms a basis for safe and accurate analysis of cyber-physical systems. We model and analyze an inverted pendulum to demonstrate that our setup can provide a significantly more accurate representation of a real system.

Call for papers: IEEE Embedded Systems Letters - Special Issue on Rigorous Modeling and Analysis of Cyber-Physical Systems

Call for papers: IEEE Embedded Systems Letters - Special Issue on Rigorous Modeling and Analysis of Cyber-Physical Systems

Special Issue on Rigorous Modeling and Analysis of Cyber-Physical Systems

Special Issue on Rigorous Modeling and Analysis of Cyber-Physical Systems