Zobrazeno 1 - 10
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pro vyhledávání: '"BAYER, MARKUS"'
Autor:
Bayer, Markus, Reuter, Christian
Active learning is designed to minimize annotation efforts by prioritizing instances that most enhance learning. However, many active learning strategies struggle with a 'cold start' problem, needing substantial initial data to be effective. This lim
Externí odkaz:
http://arxiv.org/abs/2405.10808
Publicly available information contains valuable information for Cyber Threat Intelligence (CTI). This can be used to prevent attacks that have already taken place on other systems. Ideally, only the initial attack succeeds and all subsequent ones ar
Externí odkaz:
http://arxiv.org/abs/2304.11960
The field of cybersecurity is evolving fast. Experts need to be informed about past, current and - in the best case - upcoming threats, because attacks are becoming more advanced, targets bigger and systems more complex. As this cannot be addressed m
Externí odkaz:
http://arxiv.org/abs/2212.02974
Gathering cyber threat intelligence from open sources is becoming increasingly important for maintaining and achieving a high level of security as systems become larger and more complex. However, these open sources are often subject to information ov
Externí odkaz:
http://arxiv.org/abs/2207.11076
Publikováno v:
ACM Computing Surveys (2022)
Data augmentation, the artificial creation of training data for machine learning by transformations, is a widely studied research field across machine learning disciplines. While it is useful for increasing a model's generalization capabilities, it c
Externí odkaz:
http://arxiv.org/abs/2107.03158
Autor:
Bayer, Markus, Kaufhold, Marc-André, Buchhold, Björn, Keller, Marcel, Dallmeyer, Jörg, Reuter, Christian
Publikováno v:
International Journal of Machine Learning and Cybernetics (2022)
In many cases of machine learning, research suggests that the development of training data might have a higher relevance than the choice and modelling of classifiers themselves. Thus, data augmentation methods have been developed to improve classifie
Externí odkaz:
http://arxiv.org/abs/2103.14453
Publikováno v:
In Computers & Security November 2023 134
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Akademický článek
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Autor:
BAYER, MARKUS1 bayer@peasec.tu-darmstadt.de, KAUFHOLD, MARC-ANDRÉ1 kaufhold@peasec.tu-darmstadt.de, REUTER, CHRISTIAN1 reuter@peasec.tu-darmstadt.de
Publikováno v:
ACM Computing Surveys. Jul2023, Vol. 55 Issue 7, p1-39. 39p.