References of "Verwer, Sicco"
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See detailReliable Machine Learning for Networking: Key Concerns and Approaches
Hammerschmidt, Christian UL; Garcia, Sebastian; Verwer, Sicco et al

Poster (2017, October)

Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of ... [more ▼]

Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when using machine learning as an approach to a problem in networking and translate testing performance to real networks deployments successfully. This paper, rather than presenting a particular technical result, discusses the necessary considerations to obtain good results when using machine learning to analyze network-related data. [less ▲]

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See detailHuman in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms
Hammerschmidt, Christian UL; State, Radu UL; Verwer, Sicco

Poster (2017, August)

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 recovering or reverse ... [more ▼]

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 recovering or reverse engineering the model generating the data despite noisy, incomplete, or imperfectly sampled data sources rather than optimizing a purely numeric target function. Domain expertise and human knowledge about the target domain can guide this process, and typically is captured in parameter settings. Often, domain expertise is subconscious and not expressed explicitly. Directly interacting with the learning algorithm makes it easier to utilize this knowledge effectively. [less ▲]

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See detailEfficient Learning of Communication Profiles from IP Flow Records
Hammerschmidt, Christian UL; Marchal, Samuel; Pellegrino, Gaetano et al

Poster (2016, November)

The task of network traffic monitoring has evolved drastically with the ever-increasing amount of data flowing in large scale networks. The automated analysis of this tremendous source of information ... [more ▼]

The task of network traffic monitoring has evolved drastically with the ever-increasing amount of data flowing in large scale networks. The automated analysis of this tremendous source of information often comes with using simpler models on aggregated data (e.g. IP flow records) due to time and space constraints. A step towards utilizing IP flow records more effectively are stream learning techniques. We propose a method to collect a limited yet relevant amount of data in order to learn a class of complex models, finite state machines, in real-time. These machines are used as communication profiles to fingerprint, identify or classify hosts and services and offer high detection rates while requiring less training data and thus being faster to compute than simple models. [less ▲]

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See detailBehavioral Clustering of Non-Stationary IP Flow Record Data
Hammerschmidt, Christian UL; Marchal, Samuel; State, Radu UL et al

Poster (2016, October)

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See detailFlexible State-Merging for learning (P)DFAs in Python
Hammerschmidt, Christian UL; Loos, Benjamin Laurent UL; Verwer, Sicco et al

Scientific Conference (2016, October)

We present a Python package for learning (non-)probabilistic deterministic finite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular ... [more ▼]

We present a Python package for learning (non-)probabilistic deterministic finite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular \texttt{scikit-learn} package, it is easy to use and new learning methods are easy to add. It provides PDFA learning as an additional tool for sequence prediction or classification to data scientists, without the need to understand the algorithm itself but rather the limitations of PDFA as a model. With applications of automata learning in diverse fields such as network traffic analysis, software engineering and biology, a stratified package opens opportunities for practitioners. [less ▲]

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See detailLearning Deterministic Finite Automata from Infinite Alphabets
Pellegrino, Gaetano; Hammerschmidt, Christian UL; Lin, Qin et al

Scientific Conference (2016, October)

We proposes an algorithm to learn automata infinite alphabets, or at least too large to enumerate. We apply it to define a generic model intended for regression, with transitions constrained by intervals ... [more ▼]

We proposes an algorithm to learn automata infinite alphabets, or at least too large to enumerate. We apply it to define a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red \& Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression where typical models are hard to interpret. [less ▲]

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See detailShort-term Time Series Forecasting with Regression Automata
Lin, Qin; Hammerschmidt, Christian UL; Pellegrino, Gaetano et al

Poster (2016)

We present regression automata (RA), which are novel type syntactic models for time series forecasting. Building on top of conventional state-merging algorithms for identifying automata, RA use numeric ... [more ▼]

We present regression automata (RA), which are novel type syntactic models for time series forecasting. Building on top of conventional state-merging algorithms for identifying automata, RA use numeric data in addition to symbolic values and make predictions based on this data in a regression fashion. We apply our model to the problem of hourly wind speed and wind power forecasting. Our results show that RA outperform other state-of-the-art approaches for predicting both wind speed and power generation. In both cases, short-term predictions are used for resource allocation and infrastructure load balancing. For those critical tasks, the ability to inspect and interpret the generative model RA provide is an additional benefit. [less ▲]

Detailed reference viewed: 40 (4 UL)