6 papers
PAC learning PDFA from data streams
Robert Baumgartner, Sicco Verwer
This is an extended version of our publication Learning state machines from data streams: A generic strategy and an improved heuristic, International Conference on Grammatical Infe…
FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata
Sicco Verwer, Christian Hammerschmidt
We present the efficient implementations of probabilistic deterministic finite automaton learning methods available in FlexFringe. These implement well-known strategies for state-m…
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
Jacobus G. M. van der Linden, Daniël Vos, Daniël Vos +4
Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally opti…
State Frequency Estimation for Anomaly Detection
Clinton Cao, Agathe Blaise, Annibale Panichella +1
Many works have studied the efficacy of state machines for detecting anomalies within NetFlows. These works typically learn a model from unlabeled data and compute anomaly scores f…
Automated Test-Case Generation for REST APIs Using Model Inference Search Heuristic
Clinton Cao, Annibale Panichella, Sicco Verwer
The rising popularity of the microservice architectural style has led to a growing demand for automated testing approaches tailored to these systems. EvoMaster is a state-of-the-ar…
ENCODE: Encoding NetFlows for Network Anomaly Detection
Clinton Cao, Annibale Panichella, Sicco Verwer +2
NetFlow data is a popular network log format used by many network analysts and researchers. The advantages of using NetFlow over deep packet inspection are that it is easier to col…