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arXiv 2409.17844cs.SEcs.CR
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2030年及未来的软件安全分析:研究路线图

Software Security Analysis in 2030 and Beyond: A Research Roadmap

Marcel Böhme, Eric Bodden, Tevfik Bultan, Cristian Cadar, Yang Liu, Giuseppe Scanniello

AI总结:

本文提出了未来软件安全分析的研究路线图,针对生成式AI编写代码、系统异构性和供应链依赖等新挑战,探讨了未来漏洞检测与系统防护的方法,并指出了该领域的开放性机遇与长期前景。

摘要:

随着我们的生活、商业乃至全球经济日益依赖于众多互联软件系统的安全运行,软件工程研究界面临着前所未有的研究挑战,同时也迎来了令人兴奋的新机遇。在这篇路线图论文中,我们概述了对未来软件系统进行软件安全分析的愿景。鉴于生成式AI的最新进展,我们需要新方法来评估和最大化由机器共同编写的代码的安全性。随着软件系统日益异构,我们需要即使在某些函数(例如由深度神经网络)自动生成的情况下也能实际可行的方法。随着软件系统越来越依赖软件供应链,我们需要能够扩展到整个生态系统的工具。未来系统中存在何种漏洞,我们如何检测它们?当所有浅层错误被发现后,我们如何发现深藏在系统中的漏洞?假设我们无法找到所有安全缺陷,我们如何保护我们的系统?为了回答这些问题,我们以软件安全的最新进展调查作为研究路线图的起点,随后讨论了未解决的挑战与机遇,最后对该领域的长期发展前景进行了总结。

英文摘要:

As our lives, our businesses, and indeed our world economy become increasingly reliant on the secure operation of many interconnected software systems, the software engineering research community is faced with unprecedented research challenges, but also with exciting new opportunities. In this roadmap paper, we outline our vision of Software Security Analysis for the software systems of the future. Given the recent advances in generative AI, we need new methods to evaluate and maximize the security of code co-written by machines. As our software systems become increasingly heterogeneous, we need practical approaches that work even if some functions are automatically generated, e.g., by deep neural networks. As software systems depend evermore on the software supply chain, we need tools that scale to an entire ecosystem. What kind of vulnerabilities exist in future systems and how do we detect them? When all the shallow bugs are found, how do we discover vulnerabilities hidden deeply in the system? Assuming we cannot find all security flaws, how can we nevertheless protect our system? To answer these questions, we start our research roadmap with a survey of recent advances in software security, then discuss open challenges and opportunities, and conclude with a long-term perspective for the field.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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