Deep Learning in Textual Low-Data Regimes for Cybersecurity

Häftad, Engelska, 2025

1 849 kr

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mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; Supervised machine learning offers a potential solution, but the rapidly changing nature of cyber threats renders static models ineffective and the creation of new models too labor-intensive. mso-ascii-theme-font: minor-latin;

Produktinformation

Utgivningsdatum2025-08-21
Mått148 x 210 x 21 mm
Vikt486 g
FormatHäftad
SpråkEngelska
SerieTechnology, Peace and Security I Technologie, Frieden und Sicherheit
Antal sidor347
FörlagSpringer Fachmedien Wiesbaden
ISBN9783658487775

Tillhör följande kategorier

Teknik: allmänt inom Naturvetenskap och teknik
IT-säkerhet inom Data och it
Nätverk och kommunikation inom Data och it
Systemvetenskap och AI inom Data och it

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.