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2020 自动化网络攻击报告:假设与现实 - 美国安全与新兴技术研究中心(英文版).pdf

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1、NOVEMBER 2020 Automating Cyber Attacks HYPE AND REALITY AUTHORS Ben Buchanan John Bansemer Dakota Cary Jack Lucas Micah Musser Center for Security and Emerging Technology2 Established in January 2019, the Center for Security and Emerging Technology (CSET) at Georgetowns Walsh School of Foreign Servi

2、ce is a research organization fo- cused on studying the security impacts of emerging tech- nologies, supporting academic work in security and tech- nology studies, and delivering nonpartisan analysis to the policy community. CSET aims to prepare a generation of policymakers, analysts, and diplomats

3、to address the chal- lenges and opportunities of emerging technologies. During its first two years, CSET will focus on the effects of progress in artificial intelligence and advanced computing. CSET.GEORGETOWN.EDU | CSETGEORGETOWN.EDU Automating Cyber Attacks NOVEMBER 2020 AUTHORS Ben Buchanan John

4、Bansemer Dakota Cary Jack Lucas Micah Musser HYPE AND REALITY ACKNOWLEDGMENTS The authors would like to thank Max Guise, Drew Lohn, Igor Mikolic- Torreira, Chris Rohlf, Lynne Weil, and Alexandra Vreeman for their comments on earlier versions of this manuscript. PRINT AND ELECTRONIC DISTRIBUTION RIGH

5、TS 2020 by the Center for Security and Emerging Technology. This work is licensed under a Creative Commons Attribution- NonCommercial 4.0 International License. To view a copy of this license, visit: https:/creativecommons.org/licenses/by-nc/4.0/. Cover photo: KsanaGraphica/ShutterStock. Center for

6、Security and Emerging Technologyi EXECUTIVE SUMMARY INTRODUCTION 1 | THE CYBER KILL CHAIN 2 | HOW MACHINE LEARNING CAN (AND CANT) CHANGE OFFENSIVE OPERATIONS 3 | CONCLUSION: KEY JUDGMENTS ENDNOTES III V 1 1 1 21 29 Contents Center for Security and Emerging Technologyiv Center for Security and Emergi

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本文主要探讨了机器学习在网络攻击中的应用和局限性。文章首先介绍了网络攻击的“杀伤链”模型,包括侦察、武器化、投递、指挥控制、跳转和行动目标等六个阶段。然后,文章详细讨论了机器学习如何可能改变这些阶段,包括提高社会工程攻击的规模和成功率,增强网络漏洞的发现能力,提高网络操作的隐蔽性,以及使恶意代码更独立于人类操作者。然而,文章也指出机器学习系统存在重大局限性,如对显著数据的依赖,对对抗性攻击的脆弱性,以及在部署中的复杂性。总的来说,机器学习在网络攻击中的应用被过度炒作,但仍然具有重要意义。对于大多数攻击者来说,他们不太可能明显需要用机器学习来增强他们的操作,特别是考虑到一些机器学习技术的复杂性和对相关数据的需求。
机器学习在网络攻击中的作用是什么? 自动化如何帮助网络攻击者? 机器学习在网络攻击中是否被过度炒作?
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