43 citations · 74 across the 7 of their papers we have counts for
7 papers
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…
Minority Class Oversampling for Tabular Data with Deep Generative Models
Ramiro Camino, Christian Hammerschmidt, Radu State
In practice, machine learning experts are often confronted with imbalanced data. Without accounting for the imbalance, common classifiers perform poorly and standard evaluation met…
Beyond Labeling: Using Clustering to Build Network Behavioral Profiles of Malware Families
Azqa Nadeem, Christian Hammerschmidt, Carlos H. Gañán +1
Malware family labels are known to be inconsistent. They are also black-box since they do not represent the capabilities of malware. The current state-of-the-art in malware capabil…
Improving Missing Data Imputation with Deep Generative Models
Ramiro D. Camino, Christian A. Hammerschmidt, Radu State
Datasets with missing values are very common on industry applications, and they can have a negative impact on machine learning models. Recent studies introduced solutions to the pr…
Generating Multi-Categorical Samples with Generative Adversarial Networks
Ramiro Camino, Christian Hammerschmidt, Radu State
We propose a method to train generative adversarial networks on mutivariate feature vectors representing multiple categorical values. In contrast to the continuous domain, where GA…
Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms
Christian A. Hammerschmidt, Radu State, Sicco Verwer
We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata. Learning these automata often amounts to reco…
Interpreting Finite Automata for Sequential Data
Christian Albert Hammerschmidt, Sicco Verwer, Qin Lin +1
Automaton models are often seen as interpretable models. Interpretability itself is not well defined: it remains unclear what interpretability means without first explicitly specif…