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Titlebook: Cyberdefense; The Next Generation Marcus Matthias Keupp Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license

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發(fā)表于 2025-3-21 18:03:54 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Cyberdefense
副標(biāo)題The Next Generation
編輯Marcus Matthias Keupp
視頻videohttp://file.papertrans.cn/242/241864/241864.mp4
概述Analyzes cyberdefense from a novel and interdisciplinary perspective.Explains why cyberdefense organized and performed by humans is too slow, too cumbersome, and too ineffective.Offers solutions for p
叢書名稱International Series in Operations Research & Management Science
圖書封面Titlebook: Cyberdefense; The Next Generation Marcus Matthias Keupp Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license
描述This book analyzes cyberdefense from a novel and interdisciplinary perspective, offering solutions for problems that have long impeded a more efficient defense. It explains why cyberdefense organized and performed by humans is too slow, too cumbersome, and too ineffective.?.Combining the analytical capabilities of experts in operations research and management, international security studies, economics, risk analysis, and defense management, the volume addresses these problems of current cyberdefense.?The authors present suggestions for the next generation of cyberdefense, explaining why the future defense must focus on speeding up responses, why a single response may not be enough, and why effectiveness requires foresight..This makes the book a must-read for scholars, researchers, intelligence analysts, homeland security staff, and professionals who are interested in learning more about the issues of current cyberdefense, as well assolutions for the next generation of cyberdefense..
出版日期Book 2023
關(guān)鍵詞Security breaches; Artificial intelligence; Information Sharing and Analysis Center; ISAC; Malware Infor
版次1
doihttps://doi.org/10.1007/978-3-031-30191-9
isbn_softcover978-3-031-30193-3
isbn_ebook978-3-031-30191-9Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 21:41:27 | 只看該作者
Unsupervised Attack Isolation in Cyber-physical Systems: A Competitive Test of Clustering Algorithmstertwined in such structures, operators must be able to separate incoming status data to isolate the precise location of the cyberattack. We let several unsupervised algorithms compete and analyze the extent to which they can provide fast and efficient analysis in order to support operators with thi
板凳
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發(fā)表于 2025-3-22 12:30:37 | 只看該作者
A Novel Algorithm for Informed Investment in Cybersecurity Companies and Technologies. The algorithm also incorporates exogenous variables that reflect investor preferences. The results provide investors with an optimal ranking of technologies and thus help them to make more informed decisions about companies and technologies, in particular vis-à-vis traditional portfolio theory and
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發(fā)表于 2025-3-22 15:11:09 | 只看該作者
Identifying Emerging Technologies and Influential Companies Using Network Dynamics of Patent Clusterhich predicts emerging technology by a sequential blend of machine learning and network analytics. We illustrate the capabilities of this system with patent data and discuss how it can help organizations make informed decisions.
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發(fā)表于 2025-3-23 04:04:09 | 只看該作者
Drawing with Limited Resources: Statistical Modeling of Computer Network Exploitation and Preventionf information among many irrelevant ones. We model such attacks as repeated urn draws under different distributional patterns, depending on how attackers pursue their operation. Further, we use prospect theory to model risk aversion and overconfidence among attackers. We illustrate the applicability
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發(fā)表于 2025-3-23 05:55:52 | 只看該作者
Individual Career Versus Corporate Security: A Simulation of CSO Investment Choicesgets are not necessarily associated with effective corporate protection, whereas cybersecurity breaches negatively affect individual career prospects irrespective of prior investments. In this chapter we build on the Gordon-Loeb model to develop a recursive model which simulates investment dynamics,
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