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ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios
In this way, ActiveLLM can even help other active learning strategies to overcome their cold start problem.
ThreatCrawl: A BERT-based Focused Crawler for the Cybersecurity Domain
However, while this already solves the problem of extracting the information out of documents, the search for these documents is rarely considered.
CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain
As this cannot be addressed manually, cybersecurity experts need to rely on machine learning techniques.
Common Vulnerability Scoring System Prediction based on Open Source Intelligence Information Sources
With this work, the publicly available web pages referenced in the National Vulnerability Database are analyzed and made available as sources of texts through web scraping.
Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence
Since this requires a lot of labelled data using standard training methods, we combine three different low-data regime techniques - transfer learning, data augmentation, and few-shot learning - to train a high-quality classifier from very few labelled instances.
A Survey on Data Augmentation for Text Classification
Data augmentation, the artificial creation of training data for machine learning by transformations, is a widely studied research field across machine learning disciplines.
Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers
Thus, data augmentation methods have been developed to improve classifiers by artificially created training data.
