scholarly journals Parametric Analyses of Attack-fault Trees*

2021 ◽  
Vol 182 (1) ◽  
pp. 69-94
Author(s):  
Étienne André ◽  
Didier Lime ◽  
Mathias Ramparison ◽  
Mariëlle Stoelinga

Risk assessment of cyber-physical systems, such as power plants, connected devices and IT-infrastructures has always been challenging: safety (i. e., absence of unintentional failures) and security (i. e., no disruptions due to attackers) are conditions that must be guaranteed. One of the traditional tools used to consider these problems is attack trees, a tree-based formalism inspired by fault trees, a well-known formalism used in safety engineering. In this paper we define and implement the translation of attack-fault trees (AFTs) to a new extension of timed automata, called parametric weighted timed automata. This allows us to parameterize constants such as time and discrete costs in an AFT and then, using the model-checker IMITATOR, to compute the set of parameter values such that a successful attack is possible. Moreover, we add the possibility to define counter-measures. Using the different sets of parameter values computed, different attack and fault scenarios can be deduced depending on the budget, time or computation power of the attacker, providing helpful data to select the most efficient counter-measure.

2019 ◽  
Vol 254 ◽  
pp. 113554 ◽  
Author(s):  
Haitao Zhao ◽  
Peng Jiang ◽  
Zhe Chen ◽  
Collins I. Ezeh ◽  
Yuanda Hong ◽  
...  

2020 ◽  
Vol 2 (4) ◽  
pp. 579-602
Author(s):  
Ana Pereira ◽  
Carsten Thomas

Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-Physical Systems (CPS) in application areas that cannot easily be mastered with traditional control approaches, such as autonomous driving. As a consequence, the safety of machine learning became a focus area for research in recent years. Despite very considerable advances in selected areas related to machine learning safety, shortcomings were identified on holistic approaches that take an end-to-end view on the risks associated to the engineering of ML-based control systems and their certification. Applying a classic technique of safety engineering, our paper provides a comprehensive and methodological analysis of the safety hazards that could be introduced along the ML lifecycle, and could compromise the safe operation of ML-based CPS. Identified hazards are illustrated and explained using a real-world application scenario—an autonomous shop-floor transportation vehicle. The comprehensive analysis presented in this paper is intended as a basis for future holistic approaches for safety engineering of ML-based CPS in safety-critical applications, and aims to support the focus on research onto safety hazards that are not yet adequately addressed.


2021 ◽  
Vol 2094 (4) ◽  
pp. 042062
Author(s):  
A V Gurjanov ◽  
D A Zakoldaev ◽  
I O Zharinov ◽  
O O Zharinov

Abstract Cyber-modelling is the information models simulation process describing in a mathematical and formal logic languages (phenomenon models) how cyber-physical systems interaction mechanisms are united with different control laws and parameter values. The equation complexity represented in different levels of cyber-physical production systems hierarchy and non-equations of algebra, logic, end-subtraction, vector and matrices form in a discreet and uninterrupted times are defined with an aggregated number in the industrial automatics element control loop. The cyber-modelling is done for statistic and dynamic processes and equipment states being monitored in a virtual environment fixating actual in a time interval technological data. The cyber-modelling is done with integrated calculation equipment systems with parallel physical production processes of item manufacturing. The model time faster than physical processes let prognosticate the corrections modifying control signals and phase variables of cyber-physical systems united in an assembly conveyor. The cyber-modelling advantage is an expanded number of cycles to optimize the technological processes, which are calculated with integrated calculation systems using consecutive approximation method. They describe the cyber-modelling technology and propose the information models based on phenomenon cyber-physical production processes descriptions with general control theory terms, calculations and connection for hierarchy controlling structures.


2021 ◽  
Author(s):  
Zahra Ramezani ◽  
Koen Claessen ◽  
Nicholas Smallbone ◽  
martin fabian ◽  
Knut Åkesson

<div>Cyber-physical systems (CPSs) are complex and exhibit both continuous and discrete dynamics, hence it is difficult to guarantee that they satisfy given specifications, i.e., the properties that must be fulfilled by the system. Falsification of temporal logic properties is a testing approach that searches for counterexamples of a given specification, which can be used to increase the confidence that a CPS does fulfill its specifications. Falsification can be done using random search methods or optimization methods. In this paper, a method based on combining random parameters together with considering extreme combinations of parameter values is proposed. Evaluation results on benchmark problems show that this method performs well on many of the problems. Optimization methods are needed when optimization-free methods do not perform well in falsification. The efficiency of the falsification is affected by the optimization methods used to search for inputs that might falsify the specifications. This paper presents a new optimization method for falsification, Line-search falsification, where optimization is done over line segments through a vector of inputs in the n-dimensional parameter space. The evaluation results on the benchmark problems show that using this method improves the falsification performance by reducing the number of simulations necessary to falsify a specification.</div>


Designs ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 52 ◽  
Author(s):  
Daniela Cancila ◽  
Jean-Louis Gerstenmayer ◽  
Huascar Espinoza ◽  
Roberto Passerone

Autonomous and Adaptative Cyber-Physical Systems (ACPS) represent a new knowledge frontier of converging “nano-bio-info-cogno” technologies and applications. ACPS have the ability to integrate new `mutagenic’ technologies, i.e., technologies able to cause mutations in the society. Emerging approaches, such as artificial intelligence techniques and deep learning, enable exponential speedups for supporting increasingly higher levels of autonomy and self-adaptation. In spite of this disruptive landscape, however, deployment and broader adoption of ACPS in safety-critical scenarios remains challenging. In this paper, we address some challenges that are stretching the limits of ACPS safety engineering, including tightly related aspects such as ethics and resilience. We argue that a paradigm change is needed that includes the entire socio-technical aspects, including trustworthiness, responsibility, liability, as well as the ACPS ability to learn from past events, anticipate long-term threads and recover from unexpected behaviors.


2021 ◽  
Author(s):  
Zahra Ramezani ◽  
Koen Claessen ◽  
Nicholas Smallbone ◽  
Martin Fabian ◽  
Knut Åkesson

<div>Cyber-physical systems (CPSs) are complex and exhibit both continuous and discrete dynamics, hence it is difficult to guarantee that they satisfy given specifications, i.e., the properties that must be fulfilled by the system. Falsification of temporal logic properties is a testing approach that searches for counterexamples of a given specification, which can be used to increase the confidence that a CPS does fulfill its specifications. Falsification can be done using random search methods or optimization methods. In this paper, a method based on combining random parameters together with considering extreme combinations of parameter values is proposed. Evaluation results on benchmark problems show that this method performs well on many of the problems. Optimization methods are needed when optimization-free methods do not perform well in falsification. The efficiency of the falsification is affected by the optimization methods used to search for inputs that might falsify the specifications. This paper presents a new optimization method for falsification, Line-search falsification, where optimization is done over line segments through a vector of inputs in the n-dimensional parameter space. The evaluation results on the benchmark problems show that using this method improves the falsification performance by reducing the number of simulations necessary to falsify a specification.</div>


2021 ◽  
Author(s):  
Zahra Ramezani ◽  
Koen Claessen ◽  
Nicholas Smallbone ◽  
Martin Fabian ◽  
Knut Åkesson

<div>Cyber-physical systems (CPSs) are complex and exhibit both continuous and discrete dynamics, hence it is difficult to guarantee that they satisfy given specifications, i.e., the properties that must be fulfilled by the system. Falsification of temporal logic properties is a testing approach that searches for counterexamples of a given specification, which can be used to increase the confidence that a CPS does fulfill its specifications. Falsification can be done using random search methods or optimization methods. In this paper, a method based on combining random parameters together with considering extreme combinations of parameter values is proposed. Evaluation results on benchmark problems show that this method performs well on many of the problems. Optimization methods are needed when optimization-free methods do not perform well in falsification. The efficiency of the falsification is affected by the optimization methods used to search for inputs that might falsify the specifications. This paper presents a new optimization method for falsification, Line-search falsification, where optimization is done over line segments through a vector of inputs in the n-dimensional parameter space. The evaluation results on the benchmark problems show that using this method improves the falsification performance by reducing the number of simulations necessary to falsify a specification.</div>


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