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Titlebook: Silicon Valley Cybersecurity Conference; Second Conference, S Sang-Yoon Chang,Luis Bathen,Alex J. Nelson Conference proceedings 2022 Spring

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发表于 2025-3-21 17:59:34 | 显示全部楼层 |阅读模式
书目名称Silicon Valley Cybersecurity Conference
副标题Second Conference, S
编辑Sang-Yoon Chang,Luis Bathen,Alex J. Nelson
视频videohttp://file.papertrans.cn/868/867338/867338.mp4
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Silicon Valley Cybersecurity Conference; Second Conference, S Sang-Yoon Chang,Luis Bathen,Alex J. Nelson Conference proceedings 2022 Spring
描述This book constitutes selected and revised papers from the Second Silicon Valley Cybersecurity Conference, held in San Jose, USA, in December 2021. Due to the COVID-19 pandemic the conference was held in a virtual format. .The 9 full papers and one shoprt paper presented in this volume were thoroughly reviewed and selected from 15 submissions. They present most recent research on dependability, reliability, and security to address cyber-attacks, vulnerabilities, faults, and errors in networks and systems. .Chapters 1, 4, 5, 6, and 8-10 are published open access under a CC BY license (Creative Commons Attribution 4.0 International License)..
出版日期Conference proceedings 2022
关键词access control; artificial intelligence; authentication; communication; communication systems; computer c
版次1
doihttps://doi.org/10.1007/978-3-030-96057-5
isbn_softcover978-3-030-96056-8
isbn_ebook978-3-030-96057-5Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2022
The information of publication is updating

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1865-0929 r 2021. Due to the COVID-19 pandemic the conference was held in a virtual format. .The 9 full papers and one shoprt paper presented in this volume were thoroughly reviewed and selected from 15 submissions. They present most recent research on dependability, reliability, and security to address cyber
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Security Threats in Cloud Rooted from Machine Learning-Based Resource Provisioning Systems investigation, we found that adversarial ML can target resource provisioning systems (RPS) to perform distributed attacks. Our work proposes a fake trace generator (FTG), which can be wrapped around an adversary kernel to avoid detection by the RPS and to enable the adversary to get co-located with the victim’s virtual machine.
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Security Threats in Cloud Rooted from Machine Learning-Based Resource Provisioning Systemsd with the aid of Machine Learning. Researchers have found, however, that machine learning poses new threats such as adversarial attacks. Based on our investigation, we found that adversarial ML can target resource provisioning systems (RPS) to perform distributed attacks. Our work proposes a fake t
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