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Deep Learning in Textual Low-Data Regimes for Cybersecurity : Computer Science and Engineering (German Language) - Markus Bayer

Deep Learning in Textual Low-Data Regimes for Cybersecurity

By: Markus Bayer

eText | 20 August 2025

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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.

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Non-FictionEngineering & TechnologyTechnology in GeneralMaths for EngineersComputing & I.T.Computer ScienceArtificial IntelligenceMachine LearningComputer Networking & CommunicationsNetwork SecurityEngineering in GeneralMathematics
Applied MathematicsProbability & StatisticsComputer SecurityData Encryption

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