{"product_id":"9783658487775","title":"Technology, Peace and Security I Technologie, Frieden und Sicherheit","description":"\u003ch1\u003eTechnology, Peace and Security I Technologie, Frieden und Sicherheit\u003c\/h1\u003e \u003ch2\u003eBayer, Markus\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; font-family: 'Calibri','sans-serif'; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-ansi-language: EN-US;\"\u003eIn 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:\u003cbr\u003e\u003c\/span\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; font-family: 'Calibri','sans-serif'; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-ansi-language: EN-US;\"\u003e\u003cbr\u003eData Acquisition: Leveraging active learning with advanced models like GPT-4 to optimize data labeling.\u003cbr\u003e\u003c\/span\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; font-family: 'Calibri','sans-serif'; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-ansi-language: EN-US;\"\u003ePreprocessing: Utilizing GPT-2 and GPT-3 for data augmentation to enrich and diversify datasets.\u003cbr\u003e\u003c\/span\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; font-family: 'Calibri','sans-serif'; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-ansi-language: EN-US;\"\u003eModel Selection: Developing a specialized cybersecurity language model and using multi-level transfer learning.\u003cbr\u003e\u003c\/span\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; font-family: 'Calibri','sans-serif'; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-ansi-language: EN-US;\"\u003ePrediction: Introducing a novel adversarial example generation method, grounded in explainable AI, to improve model accuracy and resilience.\u003c\/span\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer Vieweg\u003c\/p\u003e \u003cp\u003ePublication Date: 2025-08-21\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9783658487775\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-658-48778-2\u003c\/p\u003e \u003cp\u003eDimensions: 210cm x148cm\u003c\/p\u003e \u003cp\u003ePages: 347\u003c\/p\u003e ","brand":"Springer Fachmedien Wiesbaden","offers":[{"title":"Default Title","offer_id":44309785739404,"sku":"9783658487775","price":107.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783658487775.jpg?v=1773313846","url":"https:\/\/lateknightbooks.com\/products\/9783658487775","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}