Deep Learning in Textual Low-Data Regimes for Cybersecurity (Paperback) (ISBN-13: 9783658487775)

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.

Author: Markus Bayer
ISBN-10: 3658487771
ISBN-13: 9783658487775
Publisher: Springer Vieweg
Language: English
Published: 08/21/2025
Pages: 347
Format: Paperback
Weight: 0.99lbs
Size: 8.27h x 5.83w x 0.84d