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Authored
20 records found
Beyond Labeling
Using Clustering to Build Network Behavioral Profiles of Malware Families
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 capability assessment includes mostly manual approaches, which are infeasible due to the ever-increasing vo
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flexfringe
A Passive Automaton Learning Package
Finite state models, such as Mealy machines or state charts, are often used to express and specify protocol and software behavior. Consequently, these models are often used in verification, testing, and for assistance in the development and maintenance process. Reverse engineerin
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Reliable Machine Learning for Networking
Key Issues and Approaches
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 impl
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Reliable Machine Learning for Networking
Key Issues and Approaches
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 impl
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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)
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Training classifiers that are robust against adversarially modified examples is becoming increasingly important in practice. In the field of malware detection, adversaries modify malicious binary files to seem benign while preserving their malicious behavior. We report on the res
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Managed security service providers increasingly rely on machine-learning methods to exceed traditional, signature-based threat detection and classification methods. As machine-learning often improves with more data available, smaller organizations and clients find themselves at a
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Automated network traffic analysis using machine learning techniques plays an important role in managing networks and IT infrastructure. A key challenge to the correct and effective application of machine learning is dealing with non-stationary learning data sources and concept d
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Automated network traffic analysis using machine learning techniques plays an important role in managing networks and IT infrastructure. A key challenge to the correct and effective application of machine learning is dealing with non-stationary learning data sources and concept d
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We proposes an algorithm to learn automata innite alphabets, or at least too large to enumerate. We apply it to dene a generic model intended for regression, with transitions
constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learni
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We proposes an algorithm to learn automata innite alphabets, or at least too large to enumerate. We apply it to dene a generic model intended for regression, with transitions
constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learni
...
We present a Python package for learning (non-)probabilistic deterministic nite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add.
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We present a Python package for learning (non-)probabilistic deterministic nite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add.
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We present a Python package for learning (non-)probabilistic deterministic nite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add.
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We present a Python package for learning (non-)probabilistic deterministic nite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add.
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We present a Python package for learning (non-)probabilistic deterministic nite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add.
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We present a novel way to detect infected hosts and identify malware in networks by analyzing network communication statistics with state-of-the-art automata learning algorithms. The automata encode patterns of short-term interactions in known malicious hosts, and are used to obt
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We present a novel way to detect infected hosts and identify malware in networks by analyzing network communication statistics with state-of-the-art automata learning algorithms. The automata encode patterns of short-term interactions in known malicious hosts, and are used to obt
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We present a novel way to detect infected hosts and identify malware in networks by analyzing network communication statistics with state-of-the-art automata learning algorithms. The automata encode patterns of short-term interactions in known malicious hosts, and are used to obt
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We present a novel way to detect infected hosts and identify malware in networks by analyzing network communication statistics with state-of-the-art automata learning algorithms. The automata encode patterns of short-term interactions in known malicious hosts, and are used to obt
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