In today's fast-paced cybersecurity landscape, professionals are increasingly challenged by the vast volumes of cyber threat data, making it difficult to identify and mitigate threats effectively. Traditional clustering methods help in broadly categorizing threats but fall short when it comes to the fine-grained analysis necessary for precise threat management. 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. This book addresses these challenges by introducing innovative low-data regime methods that enhance the machine learning process with minimal labeled data. The proposed approach spans four key stages:
Data Acquisition: Leveraging active learning with advanced models like GPT-4 to optimize data labeling.
Preprocessing: Utilizing GPT-2 and GPT-3 for data augmentation to enrich and diversify datasets.
Model Selection: Developing a specialized cybersecurity language model and using multi-level transfer learning.
Prediction: Introducing a novel adversarial example generation method, grounded in explainable AI, to improve model accuracy and resilience.
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.
Dr. rer. nat. Markus Bayer is a research associate and post-doctoral researcher at the Chair of Science and Technology for Peace and Security (PEASEC) in the Department of Computer Science at the Technical University of Darmstadt.
