422 citations · 590 across the 5 of their papers we have counts for
5 papers
ThreatCrawl: A BERT-based Focused Crawler for the Cybersecurity Domain
Philipp Kuehn, Mike Schmidt, Markus Bayer +1
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…
CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain
Markus Bayer, Philipp Kuehn, Ramin Shanehsaz +1
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,…
Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence
Markus Bayer, Tobias Frey, Christian Reuter
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 co…
A Survey on Data Augmentation for Text Classification
Markus Bayer, Marc-André Kaufhold, Christian Reuter
Data augmentation, the artificial creation of training data for machine learning by transformations, is a widely studied research field across machine learning disciplines. While i…
Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers
Markus Bayer, Marc-André Kaufhold, Björn Buchhold +3
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. Th…