论文标题
基于机器学习的软件系统的安全：对威胁，实践和挑战的调查
Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges
论文作者
论文摘要
机器学习的快速发展（ML）在许多领域（例如计算机视觉，视频和语音识别）表现出了卓越的性能。现在，它已越来越多地在软件系统中自动化核心任务。但是，如何安全地开发基于机器的现代软件系统（MLBS）仍然是一个巨大的挑战，对此，不足的考虑将在很大程度上限制其在安全至关重要的域中的应用。一个问题是，目前的MLBSS开发往往是匆忙的，而暴露于外部用户和攻击者的潜在漏洞和隐私问题将在很大程度上被忽略，并且很难被识别。此外，基于机器学习的软件系统在不同开发阶段从需求分析到系统维护的新漏洞表现出不同的责任，这是由于其固有的模型和数据以及外部对手功能的固有限制。因此，成功产生了这样的智能系统，将共同从不同的研究领域（即软件工程，系统安全和机器学习）征求专门的努力。有关ML安全问题的最新作品大多数都集中在数据和模型上，这使对抗性攻击了。在这项工作中，我们认为基于机器学习的软件系统的安全性可能是由固有的系统缺陷或外部对抗性攻击引起的，并且在整个生命周期中应采取安全的开发实践。尽管机器学习已成为现有软件工程实践的新威胁领域，但没有这样的评论工作涵盖该主题。总体而言，我们对MLBSS的安全性进行了整体审查，该评论涵盖了从结构上对安全威胁的三个不同方面的审查的系统理解...
The rapid development of Machine Learning (ML) has demonstrated superior performance in many areas, such as computer vision, video and speech recognition. It has now been increasingly leveraged in software systems to automate the core tasks. However, how to securely develop the machine learning-based modern software systems (MLBSS) remains a big challenge, for which the insufficient consideration will largely limit its application in safety-critical domains. One concern is that the present MLBSS development tends to be rush, and the latent vulnerabilities and privacy issues exposed to external users and attackers will be largely neglected and hard to be identified. Additionally, machine learning-based software systems exhibit different liabilities towards novel vulnerabilities at different development stages from requirement analysis to system maintenance, due to its inherent limitations from the model and data and the external adversary capabilities. The successful generation of such intelligent systems will thus solicit dedicated efforts jointly from different research areas, i.e., software engineering, system security and machine learning. Most of the recent works regarding the security issues for ML have a strong focus on the data and models, which has brought adversarial attacks into consideration. In this work, we consider that security for machine learning-based software systems may arise from inherent system defects or external adversarial attacks, and the secure development practices should be taken throughout the whole lifecycle. While machine learning has become a new threat domain for existing software engineering practices, there is no such review work covering the topic. Overall, we present a holistic review regarding the security for MLBSS, which covers a systematic understanding from a structure review of three distinct aspects in terms of security threats...
