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Deep Learning in Textual Low-Data Regimes for Cybersecurity

By Markus Bayer

Deep Learning in Textual Low-Data Regimes for Cybersecurity

By Markus Bayer

0 - Default Title
Description
Introduction.- Research Design.- Findings.- Discussion.- Conclusion.- Information Overload in Crisis Management: Bilingual Evaluation of Embedding Models for Clustering Social Media Posts in Emergencies.- ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios.- A Survey on Data Augmentation for Text Classification.- Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers.- Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter.- CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain.- Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence.- XAI-Attack: Utilizing Explainable AI to Find Incorrectly Learned Patterns for Black-Box Adversarial Example Creation.
Product details
Binding:
Paperback
Number of Pages:
376
Release Date:
2025-08-21
Publication Date:
2025-08-21
Publisher:
Springer Vieweg
Languages:
Original: English
ISBN10:
3658487771
ISBN13:
9783658487775
GPSR Manufacturer Reference:
[email protected]
Weight:
486 g
Height:
148 cm
Width:
210 cm
Thickness:
21 cm
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