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Titlebook: Network Security Empowered by Artificial Intelligence; Yingying Chen,Jie Wu,Xiaogang Wang Book 2024 The Editor(s) (if applicable) and The

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樓主: 兇惡的老婦
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發(fā)表于 2025-3-25 03:22:24 | 只看該作者
22#
發(fā)表于 2025-3-25 09:33:18 | 只看該作者
Jiacheng Shangreds of exercises, with many designed to help master techniq.The first monograph on singularities of mappings for many years, this book provides an introduction to the subject and an account of recent developments concerning the local structure of complex analytic mappings..Part I of the book develo
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發(fā)表于 2025-3-25 12:10:04 | 只看該作者
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發(fā)表于 2025-3-25 17:04:48 | 只看該作者
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發(fā)表于 2025-3-25 22:17:21 | 只看該作者
Agnideven Palanisamy Sundar,Feng Li,Xukai Zou,Tianchong Gaonts concerning the local structure of complex analytic mappings..Part I of the book develops the now classical real C.∞. and complex analytic theories jointly. Standard topics such as stability, deformation theory and finite determinacy, are covered in this part. In Part II of the book, the authors
26#
發(fā)表于 2025-3-26 00:21:03 | 只看該作者
27#
發(fā)表于 2025-3-26 05:23:10 | 只看該作者
nCore: Clean Slate Next-G Mobile Core Network Architecture for Scalability and Low Latencye requirements of 5G radio access technologies and beyond. The current 3GPP specification for 5G mobile networks has evolved through two generations of cellular (3G and 4G), and is fundamentally limited by its evolutionary design which incorporates legacy signaling from switched telecom networks int
28#
發(fā)表于 2025-3-26 09:06:50 | 只看該作者
Decision-Dominant Strategic Defense Against Lateral Movement for 5G Zero-Trust Multi-Domain Networkseate a highly interconnected battle network that is difficult for adversaries to disrupt or defeat. However, the adoption of 5G technologies on battlefields presents new vulnerabilities due to the complexity of interconnections and the diversity of software, hardware, and devices from different supp
29#
發(fā)表于 2025-3-26 13:30:44 | 只看該作者
Artificial Intelligence and Machine Learning for Network Security: Quo Vadis?be protected with basic network monitoring and analysis tools and traditional network intrusion detection systems. As a result, artificial-intelligence (AI) and machine learning (ML) is increasingly becoming the focal point of many advancements in network intrusion detection systems. Consequently, i
30#
發(fā)表于 2025-3-26 18:25:43 | 只看該作者
Understanding the Ineffectiveness of the Transfer Attack in Intrusion Detection Systemackers can create AEs to make the detection system predict wrong network activities. Existing attacks only add a small perturbation to revise the network packets to obtain a high attack effectiveness. However, these AEs are crafted based on the white-box setting. It is unclear if such AEs can transf
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