arXiv subjects

Yiannis Papadopoulos

Publications and source records attributed to Yiannis Papadopoulos.

23 records · Page 2Linked to original sources

arXiv2021

DEIS: Dependability Engineering Innovation for Industrial CPS

The open and cooperative nature of Cyber-Physical Systems (CPS) poses new challenges in assuring dependability. The DEIS project (Dependability Engineering Innovation for automotive CPS. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 732242, see http://www.deis-project.eu) addresses these challenges by developing technologies that form a science of dependable system integration. In the core of these technologies lies the concept of a Digital Dependability Identity (DDI) of a component or system. DDIs are modular, composable, and executable in the field facilitating (a) efficient synthesis of component and system dependability information over the supply chain and (b) effective evaluation of this information in-the-field for safe and secure composition of highly distributed and autonomous CPS. The paper outlines the DDI concept and opportunities for application in four industrial use cases.

cs.SE↗

arXiv2021

WAP: Digital Dependability Identities

Cyber-Physical Systems (CPS) provide enormous potential for innovation but a precondition for this is that the issue of dependability has been addressed. This paper presents the concept of a Digital Dependability Identity (DDI) of a component or system as foundation for assuring the dependability of CPS. A DDI is an analyzable and potentially executable model of information about the dependability of a component or system. We argue that DDIs must fulfill a number of properties including being universally useful across supply chains, enabling off-line certification of systems where possible, and providing capabilities for in-field certification of safety of CPS. In this paper, we focus on system safety as one integral part of dependability and as a practical demonstration of the concept, we present an initial implementation of DDIs in the form of Conditional Safety Certificates (also known as ConSerts). We explain ConSerts and their practical operationalization based on an illustrative example.

cs.OH↗

arXiv2020

Failure Mode Reasoning in Model Based Safety Analysis

Failure Mode Reasoning (FMR) is a novel approach for analyzing failure in a Safety Instrumented System (SIS). The method uses an automatic analysis of an SIS program to calculate potential failures in parts of the SIS. In this paper we use a case study from the power industry to demonstrate how FMR can be utilized in conjunction with other model-based safety analysis methods, such as HiP-HOPS and CFT, in order to achieve a comprehensive safety analysis of SIS. In this case study, FMR covers the analysis of SIS inputs while HiP-HOPS/CFT models the faults of logic solver and final elements. The SIS program is analyzed by FMR and the results are exported to HiP-HOPS/CFT via automated interfaces. The final outcome is the collective list of SIS failure modes along with their reliability measures. We present and review the results from both qualitative and quantitative perspectives.

cs.SE↗

arXiv2020

SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with an exclusive testing approach of otherwise inaccessible black-box systems. Especially the interaction between safety and security is a central challenge, as security violations can lead to compromised safety. The contribution of this paper to addressing both safety and security within a single concept of protection applicable during the operation of ML systems is active monitoring of the behaviour and the operational context of the data-driven system based on distance measures of the Empirical Cumulative Distribution Function (ECDF). We investigate abstract datasets (XOR, Spiral, Circle) and current security-specific datasets for intrusion detection (CICIDS2017) of simulated network traffic, using distributional shift detection measures including the Kolmogorov-Smirnov, Kuiper, Anderson-Darling, Wasserstein and mixed Wasserstein-Anderson-Darling measures. Our preliminary findings indicate that the approach can provide a basis for detecting whether the application context of an ML component is valid in the safety-security. Our preliminary code and results are available at https://github.com/ISorokos/SafeML.

cs.LG↗

arXiv2019

PeSOA: Penguins Search Optimisation Algorithm for Global Optimisation Problems

This paper develops Penguin search Optimisation Algorithm (PeSOA), a new metaheuristic algorithm which is inspired by the foraging behaviours of penguins. A population of penguins located in the solution space of the given search and optimisation problem is divided into groups and tasked with finding optimal solutions. The penguins of a group perform simultaneous dives and work as a team to collaboratively feed on fish the energy content of which corresponds to the fitness of candidate solutions. Fish stocks have higher fitness and concentration near areas of solution optima and thus drive the search. Penguins can migrate to other places if their original habitat lacks food. We identify two forms of penguin communication both intra-group and inter-group which are useful in designing intensification and diversification strategies. An efficient intensification strategy allows fast convergence to a local optimum, whereas an effective diversification strategy avoids cyclic behaviour around local optima and explores more effectively the space of potential solutions. The proposed PeSOA algorithm has been validated on a well-known set of benchmark functions. Comparative performances with six other nature-inspired metaheuristics show that the PeSOA performs favourably in these tests. A run-time analysis shows that the performance obtained by the PeSOA is very stable at any time of the evolution horizon, making the PeSOA a viable approach for real world applications.

cs.NE↗