Related papers: STPA for Learning-Enabled Systems: A Survey and A …
This research considers the problem of identifying safety constraints and developing Run Time Assurance (RTA) for Deep Reinforcement Learning (RL) Tactical Autopilots that use neural network control systems (NNCS). This research studies a…
Identifying potential social and ethical risks in emerging machine learning (ML) models and their applications remains challenging. In this work, we applied two well-established safety engineering frameworks (FMEA, STPA) to a case study…
The in-vehicle diagnostic and software update system, which supports remote diagnostic and Over-The-Air (OTA) software updates, is a critical attack goal in automobiles. Adversaries can inject malicious software into vehicles or steal…
Systems Theoretic Process Analysis (STPA) is a widely recommended method for analysing complex system safety. STPA can identify numerous Unsafe Control Actions (UCAs) and requirements depending on the level of granularity of the analysis…
Context: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. Objective: This paper aims to deliver a comprehensive overview of the current status quo…
Runtime verification or runtime monitoring equips safety-critical cyber-physical systems to augment design assurance measures and ensure operational safety and security. Cyber-physical systems have interaction failures, attack surfaces, and…
Teleoperation is becoming an essential feature in automated vehicle concepts, as it will help the industry overcome challenges facing automated vehicles today. Teleoperation follows the idea to get humans back into the loop for certain rare…
Machine learning (ML) pervades an increasing number of academic disciplines and industries. Its impact is profound, and several fields have been fundamentally altered by it, autonomy and computer vision for example; reliability engineering…
Robotic Process Automation (RPA) is the automation of rule-based routine processes to increase efficiency and to reduce costs. Due to the utmost importance of process automation in industry, RPA attracts increasing attention in the…
Simulation-based probabilistic risk assessment (SPRA) is a systematic and comprehensive methodology that has been used and refined over the past few decades to evaluate the risks associated with complex systems. SPRA models are well…
Recently, large language models (LLMs) have achieved widespread application across various fields. Despite their impressive capabilities, LLMs suffer from a lack of structured reasoning ability, particularly for complex tasks requiring…
Model checking is a proven approach for checking whether the behavior model of a safety-critical system fulfills safety properties that are stated as LTL formulas.We propose rules for generating such LTL formulas automatically based on the…
Machine Learning (ML) models are increasingly integrated into safety-critical systems, such as autonomous vehicle platooning, to enable real-time decision-making. However, their inherent imperfection introduces a new class of failure:…
Intrusion detection systems are crucial for network security. Verification of these systems is complicated by various factors, including the heterogeneity of network platforms and the continuously changing landscape of cyber threats. In…
Robotic telesurgical systems are one of the most complex medical cyber-physical systems on the market, and have been used in over 1.75 million procedures during the last decade. Despite significant improvements in design of robotic surgical…
Probabilistic security assessment and real-time dynamic security assessments (DSA) are promising to better handle the risks of system operations. The current methodologies of security assessments may require many time-domain simulations for…
According to the latest provisional statistics released by the UK Department for Transport, Great Britain recorded 1,633 road deaths in 2024, representing a slight increase from 2023 and raising concerns about safety progress, which…
Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance. Many of these systems, in domains such as healthcare , automotive and manufacturing, exhibit high…
In system development, epistemic uncertainty is an ever-present possibility when reasoning about the causal factors during hazard analysis. Such uncertainty is common when complicated systems interact with one another, and it is dangerous…
Context: The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. However, the full potential of machine learning for…