Cyber-Physical Systems (CPS) are susceptible to various anomalies during their operations.
Thus, it is important to detect such anomalies. Detecting such anomalies is challenging …

Anomaly detection is critical to ensure the security of cyber-physical systems (CPS).
However, due to the increasing complexity of attacks and CPS themselves, anomaly …

Active automata learning is a technique to automatically infer behavioral models of black-
box systems. Today's learning algorithms enable the deduction of models that describe …

We present Mealy machines with a single timer (MM1Ts), a class of sufficiently expressive
models to describe the real-time behavior of many realistic applications that we can learn …

Safety is still one of the major research challenges in reinforcement learning (RL). In this
paper, we address the problem of how to avoid safety violations of RL agents during …

Automata learning enables model-based analysis of black-box systems by automatically
constructing models from system observations, which are often collected via testing. The …

Automata-based modeling of hybrid and cyber-physical systems (CPS) is an important
formal abstraction amenable to algorithmic analysis of its dynamic behaviors, such as in …

Black-box systems are inherently hard to verify. Many verification techniques, like model
checking, require formal models as a basis. However, such models often do not exist, or they …

We present an algorithm to learn a deterministic timed automaton (DTA) via membership
and equivalence queries. Our algorithm is an extension of the L* algorithm with a Myhill …

Active automata learning became a popular tool for the behavioral analysis of
communication protocols. The main advantage is that no manual modeling effort is required …