Finding Unknown Unknowns using Cyber-Physical System Simulators (Extended Report)
Simulation-based approaches are among the most practical means to search for safety violations, bugs, and other unexpected events in cyber-physical systems (CPS). Where existing approaches search for simulations violating a formal specification or maximizing a notion of coverage, in this work we propose a new goal for testing: to discover unknown rare behaviors by examining discrete mode sequences. We assume a CPS simulator outputs mode information, and strive to explore the sequences of modes produced by varying the initial state or time-varying uncertainties. We hypothesize that rare mode sequences are often the most interesting to a designer, and we develop two accelerated sampling algorithms that speed up the process of finding such sequences. We evaluate our approach on several benchmarks, ranging from synthetic examples to Simulink diagrams of a CPS, demonstrating in some cases a speedup of over 100x compared with a random sampling strategy.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Detection of Unknown-Unknowns in Human-in-Plant Human-in-Loop Systems Using Physics Guided Process Models
Unknown-unknowns are operational scenarios in systems that are not accounted for in the design and test phase. In such scenarios, the operational behavior of the Human-in-loop (HIL) Human-in-Plant (HIP) systems is not gu…
Generative ODE Modeling with Known Unknowns
In several crucial applications, domain knowledge is encoded by a system of ordinary differential equations (ODE), often stemming from underlying physical and biological processes. A motivating example is intensive care …
Known UnknownsTime Series AnalysisKnowledge-enhanced Neuro-Symbolic AI for Cybersecurity and Privacy
Neuro-Symbolic Artificial Intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and explicit, symbolic knowledge contained in knowledge graphs to e…
Knowledge GraphsSolving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns?
Large Language Models (LLMs) have demonstrated remarkable performance in solving math problems, a hallmark of human intelligence. Despite high success rates on current benchmarks; however, these often feature simple prob…
MathIdentifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration
Predictive models deployed in the real world may assign incorrect labels to instances with high confidence. Such errors or unknown unknowns are rooted in model incompleteness, and typically arise because of the mismatch …