Deep Learning in Textual Low-Data Regimes for Cybersecurity
Language: English
Published by Springer, Berlin, Springer Fachmedien Wiesbaden, Springer Vieweg, 2025
- Softcover
- New
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
AbeBooks seller since January 11, 2012
Condition: New
£ 102.42
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Add to basketItem description from seller
This item is printed on demand - it takes 3-4 days longer - Neuware -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. 347 pp. Englisch.…
Seller Inventory # 9783658487775
- Title
- Deep Learning in Textual Low-Data Regimes for Cybersecurity
- Author
- Markus Bayer
- Publisher
- Springer, Berlin, Springer Fachmedien Wiesbaden, Springer Vieweg
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3658487771
- ISBN 13
- 9783658487775
- Item weight
- 486 grams
- Dimensions
- 210x148x20 mm
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.
BuchWeltWeit Ludwig Meier e.K.
Bergisch Gladbach, Germany
AbeBooks seller since January 11, 2012
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