Deep Learning in Textual Low-Data Regimes for Cybersecurity (Paperback)
Language: English
Published by Springer Fachmedien Wiesbaden, Weisbaden, 2025
- Softcover
- New
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Paperback. 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. mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; 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. mso-ascii-theme-font: minor-latin; This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
Seller Inventory # 9783658487775
- Title
- Deep Learning in Textual Low-Data Regimes for Cybersecurity (Paperback)
- Author
- Markus Bayer
- Publisher
- Springer Fachmedien Wiesbaden, Weisbaden
- Publication year
- 2025
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 10
- 3658487771
- 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.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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