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Deep Learning in Textual Low-Data Regimes for Cybersecurity

Markus Bayer

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Paperback

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English
Springer Vieweg
21 August 2025
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.
By:  
Imprint:   Springer Vieweg
Country of Publication:   Germany
Dimensions:   Height: 210mm,  Width: 148mm, 
ISBN:   9783658487775
ISBN 10:   3658487771
Series:   Technology, Peace and Security I Technologie, Frieden und Sicherheit
Pages:   347
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active

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.

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