Requirements Of Cyber-physical Systems Research Articles

Key Concepts of Systemological Approach to CPS Adaptive Information Security Monitoring

Modern cyber-physical systems (CPS) use digital control of physical processes. This allows attackers to conduct various cyberattacks on these systems. According to the current trends, an information security monitoring system (ISMS) becomes part of a security management system of CPS. It provides information to make a decision and generate a response. A large number of new methods are aimed at CPS security, including security assessment, intrusion detection, and ensuring sustainability. However, as a cyber-physical system operates over time, its structure and requirements may change. The datasets available for the protection object (CPS) and the security requirements have become dynamic. This dynamic effect causes asymmetry between the monitoring data collection and processing subsystem and the presented security tasks. The problem herein is the choice of the most appropriate set of methods in order to solve the security problems of a particular CPS configuration from a particular bank of the available methods. To solve this problem, the authors present a method for the management of an adaptive information security monitoring system. The method consists of solving a multicriteria discrete optimization problem under Pareto-optimality conditions when the available data, methods or external requirements change. The experimental study was performed on an example of smart home intrusion detection. In the study, the introduction of a constraint (a change in requirements) led to the revision of the monitoring scheme and a different recommendation of the monitoring method. As a result, the information security monitoring system gains the property of adaptability to changes in tasks and the available data. An important result from the study is the fact that the monitoring scheme obtained using the proposed management method has a proven optimality under the given conditions. Therefore, the asymmetry between the information security monitoring data collection and processing subsystem and the set of security requirements in cyber-physical systems can be overcome.

CARMEN: A framework for the verification and diagnosis of the specification of security requirements in cyber-physical systems

In the last years, cyber-physical systems (CPS) are receiving substantial mainstream attention especially in industrial environments, but this popularity has been accompanied by serious security challenges. A CPS is a complex system that includes hardware and software components, with different suppliers and connection protocols, forcing complex data management and storage. For this reason, the construction, verification and diagnosis of security CPS become a major challenge, which involves a correct specification of security requirements, the verification of the correct system configurations, and if necessary, the diagnosis to detect the features to be modified to obtain a security configuration. In this paper, we propose a framework for the verification and diagnosis of security requirements, according to the possible correct configurations of the CPS. The framework is based on the specification of the security requirements and their analysis supported by Model-Driven Engineering and Software Product Line Engineering (SPLE) approaches. To illustrate the usefulness, the proposal has been applied to the security requirements in an Agriculture 4.0 scenario based on automated hydroponic cultivation.

Robust online monitoring of signal temporal logic

Signal temporal logic (STL) is a formalism used to rigorously specify requirements of cyberphysical systems (CPS), i.e., systems mixing digital or discrete components in interaction with a continuous environment or analog components. STL is naturally equipped with a quantitative semantics which can be used for various purposes: from assessing the robustness of a specification to guiding searches over the input and parameter space with the goal of falsifying the given property over system behaviors. Algorithms have been proposed and implemented for offline computation of such quantitative semantics, but only few methods exist for an online setting, where one would want to monitor the satisfaction of a formula during simulation. In this paper, we formalize a semantics for robust online monitoring of partial traces, i.e., traces for which there might not be enough data to decide the Boolean satisfaction (and to compute its quantitative counterpart). We propose an efficient algorithm to compute it and demonstrate its usage on two large scale real-world case studies coming from the automotive domain and from CPS education in a Massively Open Online Course setting. We show that savings in computationally expensive simulations far outweigh any overheads incurred by an online approach.