scholarly journals Safety Analysis of AADL Models for Grid Cyber-Physical Systems via Model Checking of Stochastic Games

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 212 ◽  
Author(s):  
Xiaomin Wei ◽  
Yunwei Dong ◽  
Pengpeng Sun ◽  
Mingrui Xiao
Keyword(s):  
Model Checking ◽  
Stochastic Games ◽  
Safety Analysis ◽  
Cyber Physical Systems ◽  
System Level ◽  
Probabilistic Model Checking ◽  
Critical Systems ◽  
System Safety ◽  
Physical Systems ◽  
Analysis And Design

As safety-critical systems, grid cyber-physical systems (GCPSs) are required to ensure the safety of power-related systems. However, in many cases, GCPSs may be subject to uncertain and nondeterministic environmental hazards, as well as the variable quality of devices. They can cause failures and hazards in the whole system and may jeopardize system safety. Thus, it necessitates safety analysis for system safety assurance. This paper proposes an architecture-level safety analysis approach for GCPSs applying the probabilistic model-checking of stochastic games. GCPSs are modeled using Architecture Analysis and Design Language (AADL). Random errors and failures of a GCPS and nondeterministic environment behaviors are explicitly described with AADL annexes. A GCPS AADL model including the environment can be regarded as a game. To transform AADL models to stochastic multi-player games (SMGs) models, model transformation rules are proposed and the completeness and consistency of rules are proved. Property formulae are formulated for formal verification of GCPS SMG models, so that occurrence probabilities of failed states and hazards can be obtained for system-level safety analysis. Finally, a modified IEEE 9-bus system with grid elements that are power management systems is modeled and analyzed using the proposed approach.

scholarly journals Counterexample Generation for Probabilistic Model Checking Micro-Scale Cyber-Physical Systems

Micromachines ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1059
Author(s):  
Yang Liu ◽  
Yan Ma ◽  
Yongsheng Yang ◽  
Tingting Zheng
Keyword(s):  
Genetic Algorithm ◽  
Model Checking ◽  
Probabilistic Model ◽  
Cyber Physical Systems ◽  
Probabilistic Model Checking ◽  
Dynamic Power Management ◽  
Search Range ◽  
Physical Systems ◽  
Micro Scale ◽  
Markov Decision

Micro-scale Cyber-Physical Systems (MCPSs) can be automatically and formally estimated by probabilistic model checking, on the level of system model MDPs (Markov Decision Processes) against desired requirements in PCTL (Probabilistic Computation Tree Logic). The counterexamples in probabilistic model checking are witnesses of requirements violation, which can provide the meaningful information for debugging, control, and synthesis of MCPSs. Solving the smallest counterexample for probabilistic model checking MDP has been proven to be an NPC (Non-deterministic Polynomial complete) problem. Although some heuristic methods are designed for this, it is usually difficult to fix the heuristic functions. In this paper, the Genetic algorithm optimized with heuristic, i.e., the heuristic Genetic algorithm, is firstly proposed to generate a counterexample for the probabilistic model checking MDP model of MCPSs. The diagnostic subgraph serves as a compact counterexample, and diagnostic paths of MDP constitute an AND/OR tree for constructing a diagnostic subgraph. Indirect path coding of the Genetic algorithm is used to extend the search range of the state space, and a heuristic crossover operator is used to generate more effective diagnostic paths. A prototype tool based on the probabilistic model checker PAT is developed, and some cases (dynamic power management and some communication protocols) are used to illustrate its feasibility and efficiency.

scholarly journals A spatio-temporal specification language and its completeness & decidability

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Tengfei Li ◽  
Jing Liu ◽  
Haiying Sun ◽  
Xiang Chen ◽  
Lipeng Zhang ◽  
...  
Keyword(s):  
Intelligent Transportation System ◽  
Cyber Physical Systems ◽  
Specification Language ◽  
Finite Model ◽  
Critical Systems ◽  
Physical Systems ◽  
Spatial Objects ◽  
Temporal Properties ◽  
Spatio Temporal ◽  
Temporal Specification

AbstractIn the past few years, significant progress has been made on spatio-temporal cyber-physical systems in achieving spatio-temporal properties on several long-standing tasks. With the broader specification of spatio-temporal properties on various applications, the concerns over their spatio-temporal logics have been raised in public, especially after the widely reported safety-critical systems involving self-driving cars, intelligent transportation system, image processing. In this paper, we present a spatio-temporal specification language, STSL PC, by combining Signal Temporal Logic (STL) with a spatial logic S4 u, to characterize spatio-temporal dynamic behaviors of cyber-physical systems. This language is highly expressive: it allows the description of quantitative signals, by expressing spatio-temporal traces over real valued signals in dense time, and Boolean signals, by constraining values of spatial objects across threshold predicates. STSL PC combines the power of temporal modalities and spatial operators, and enjoys important properties such as finite model property. We provide a Hilbert-style axiomatization for the proposed STSL PC and prove the soundness and completeness by the spatio-temporal extension of maximal consistent set and canonical model. Further, we demonstrate the decidability of STSL PC and analyze the complexity of STSL PC. Besides, we generalize STSL to the evolution of spatial objects over time, called STSL OC, and provide the proof of its axiomatization system and decidability.

Cyber Physical Security Solutions for Pervasive Health Monitoring Systems

2013 ◽  
pp. 143-162 ◽  
Author(s):  
Krishna K. Venkatasubramanian ◽  
Sidharth Nabar ◽  
Sandeep K. S. Gupta ◽  
Radha Poovendran
Keyword(s):  
Health Monitoring ◽  
Key Agreement ◽  
Cyber Physical Systems ◽  
Monitoring Systems ◽  
Critical Systems ◽  
Physical Security ◽  
Physical Systems ◽  
Physiological Signal ◽  
Health Monitoring Systems ◽  
Pervasive Health

With a rapidly aging population, the healthcare community will soon face severe medical personnel shortage and rising costs. Pervasive Health Monitoring Systems (PHMS) can help alleviate this situation. PHMS provides continuous real-time monitoring of a person’s health using a (usually wireless) network of medical and ambient sensors/devices on the host (patients), called Body Area Networks (BANs). The sensitive nature of health information collected by PHMS mandates that patient’s privacy be protected by securing the medical data from any unauthorized access. The authors’ approach for addressing these issues focuses on a key observation that PHMS are cyber-physical systems (CPS). Cyber-physical systems are networked, computational platforms, deeply embedded in specific physical processes for monitoring and actuation purposes. In this work, they therefore present a novel perspective on securing PHMS, called Cyber Physical Security (CYPSec) solutions. CYPSec solutions are environmentally-coupled security solutions, which operate by combining traditional security primitives along with environmental features. Its use results in not only secure operation of a system but also the emergence of additional “allied” properties which enhance its overall capabilities. The principal focus of this chapter is the development of a new security approach for PHMS called CYPsec that leverages their cyber-physical nature. The authors illustrate the design issues and principals of CYPSec through two specific examples of this generic approach: (a) Physiological Signal based key Agreement (PSKA) is designed to enable automated key agreement between sensors in the BAN based on physiological signals from the body; and (b) Criticality Aware Access Control (CAAC) which has the ability to provide controlled opening of the system for emergency management. Further, they also discuss aspects such as altered threat-model, increased complexity, non-determinism, and mixed critical systems, that must be addressed to make CYPSec a reality.

scholarly journals Robust Self-Triggered Control for Nonlinear Cyber-Physical Systems with State Constraints under DoS Attacks

2021 ◽  
Author(s):  
Zhaoyang Cuan ◽  
Dawei Ding ◽  
Heng Wang
Keyword(s):  
Control Strategy ◽  
State Constraints ◽  
Denial Of Service ◽  
Cyber Physical Systems ◽  
Dos Attacks ◽  
System Safety ◽  
Physical Systems ◽  
Security Control ◽  
Event Based ◽  
Safety And Stability

Abstract This paper is concerned with the event-based control problem for nonlinear cyber-physical systems (CPSs) with state constraints. A novel security control strategy consisting of a self-triggered mechanism is developed to decrease the network communication loads to the most extent on the basis of ensuring system safety and stability. The maximum capability of the designed self-triggered mechanism to resist denial-of-service (DoS) attacks occurring in controller-actuator (C-A) and sensor-controller (S-C) channels synchronously is also analyzed. In particular, we prove that the security control strategy guarantees the system safety and stability without resulting in Zeno behavior. Finally, a numerical example is provided to demonstrate the prominent effectiveness and the advantages over the existing results.

scholarly journals A Novel Method for Safety Analysis of Cyber-Physical Systems—Application to a Ship Exhaust Gas Scrubber System

Safety ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 26 ◽  
Author(s):  
Victor Bolbot ◽  
Gerasimos Theotokatos ◽  
Evangelos Boulougouris ◽  
George Psarros ◽  
Rainer Hamann
Keyword(s):  
Failure Rate ◽  
Process Analysis ◽  
Fault Tree ◽  
Safety Analysis ◽  
Cyber Physical Systems ◽  
Open Loop ◽  
Exhaust Gas ◽  
Maritime Industry ◽  
Physical Systems ◽  
Novel Method

Cyber-Physical Systems (CPSs) represent a systems category developed and promoted in the maritime industry to automate functions and system operations. In this study, a novel Combinatorial Approach for Safety Analysis is presented, which addresses the traditional safety methods’ limitations by integrating System Theoretic Process Analysis (STPA), Events Sequence Identification (ETI) and Fault Tree Analysis (FTA). The developed method results in the development of a detailed Fault Tree that captures the effects of both the physical components/subsystems and the software functions’ failures. The quantitative step of the method employs the components’ failure rates to calculate the top event failure rate along with importance metrics for identifying the most critical components/functions. This method is implemented for an exhaust gas open loop scrubber system safety analysis to estimate its failure rate and identify critical failures considering the baseline system configuration as well as various alternatives with advanced functions for monitoring and diagnostics. The results demonstrate that configurations with SOx sensor continuous monitoring or scrubber unit failure diagnosis/prognosis lead to significantly lower failure rate. Based on the analysis results, the advantages/disadvantages of the novel method are also discussed. This study also provides insights for better safety analysis of the CPSs.

Machine Learning in Cyber-Physical Systems in Industry 4.0

2021 ◽  
pp. 20-41
Author(s):  
Rania Salih Ahmed ◽  
Elmustafa Sayed Ali Ahmed ◽  
Rashid A. Saeed
Keyword(s):  
Industry 4.0 ◽  
Large Scale ◽  
Domain Adaptation ◽  
Computation Time ◽  
Cyber Physical Systems ◽  
Modern World ◽  
Critical Systems ◽  
Physical Systems ◽  
Intelligent Models ◽  
Definition Of

Cyber-physical systems (CPS) have emerged with development of most great applications in the modern world due to their ability to integrate computation, networking, and physical process. CPS and ML applications are widely used in Industry 4.0, military, robotics, and physical security. Development of ML techniques in CPS is strongly linked according to the definition of CPS that states CPS is the mechanism of monitoring and controlling processes using computer-based algorithms. Optimizations adopted with ML in CPS include domain adaptation and fine tuning of current systems, boosting, introducing more safety and robustness by detection and reduction of vulnerabilities, and reducing computation time in time-critical systems. Generally, ML helps CPS to learn and adapt using intelligent models that are generated from training of large-scale data after processing and analysis.

Sign in / Sign up

Export Citation Format

Share Document