Bayer M. Deep Learning in Textual Low-Data Regimes for Cybersecurity 2025

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Bayer M. Deep Learning in Textual Low-Data Regimes for Cybersecurity 2025

Textbook in PDF format 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. Deep Learning, a cornerstone of modern computational solutions, is revolutionizing fields ranging from decision-making and data analysis to data categorization. Cybersecurity, in particular, is one area that benefits from the capabilities of natural language processing (NLP) with Deep Learning. Integrating it into cybersecurity practices has become progressively essential in detecting, preventing, and mitigating sophisticated cyber threats and vulnerabilities prevalent today. As cyberattacks become increasingly intricate, pervasive, and dynamic, traditional rule-based or signature-based detection methods are proving insufficient. Deep Learning, in contrast, shines with its capacity for automatic feature extraction, enabling substantial performance gains. Yet, access to vast computing resources and extensive data is imperative for Deep Learning to exhibit its full potential; however, extensive data is limited in low-data regimes. In this thesis, a low-data regime in Machine Learning refers to scenarios specific to textual data where only a limited amount of labeled data is available for training models, irrespective of the volume of unlabeled or unsupervised data