Deep Learning for Cybersecurity: Assembly Code and Authorship Analytics

网络安全深度学习：汇编代码和作者分析

• 批准号: RGPIN-2018-03872
• 负责人: Fung, Benjamin
• 金额: $ 6.99万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=757233
• 关键词: Deep; Learning; Cybersecurity; Assembly; Code

E-commerce sales in Canada are expected to reach $40 billion by 2018.1 Approximately 75% of Canadians use Internet banking.2 Clearly, the Internet and many online information systems have become part of the critical infrastructure of Canada. However, statistics also show that Canadians individuals, industry, and government are not well-prepared for new waves of cyber threats. Deep learning is a major advancement of artificial neural network (ANN), which is a machine learning framework inspired by how the human brain processes information. Due to improvement in high-performance computing, availability of huge volumes of data, and ANN, deep learning has recently shown many promising breakthroughs in multiple areas, from playing chess to self-driving cars. How can we utilize the advancement of deep learning to strengthen the security of our cyberspace and data privacy?The long-term objective of this research program is to enhance the large-scale data analytic capabilities of the cybersecurity and privacy communities so that security professionals can efficiently respond to security incidents and data custodians can effectively protect their clients' data privacy. In this 5-year research program I will focus on the following two short-term objectives.The first objective is to enhance the cybersecurity professionals' deep learning capability on assembly code analytics and provide them a new generation of software reverse engineering methods and tools to efficiently understand the inner workings of benign software and malware binaries. Specifically, the team will develop an assembly code search engine and a description generator using deep learning technology. The applicant closely collaborates with cybersecurity professionals in both public and private sectors; the research results will directly enhance the security of Canadian cyberspace. The projects will be open source; therefore, the results will also benefit the software reverse engineering and machine-learning communities.The second objective is to provide data custodians or individual Internet users with the capability of releasing digital textual documents, e.g., product reviews, opinion articles, blogs, etc., without compromising the identity of the authors due to their digital writing styles. The deliverable will include an open-source privacy-preserving text paraphrasing engine. The research result will enhance the anonymity of users in social media, which in turn promotes web freedom and the fight against social media censorship. The research result will also provide data custodians with an addition layer of privacy protection when they share their administered textual data. This will also indirectly contribute to the open data movement. 1 www.pfsweb.com/blog/2016-canada-ecommerce-market2 www.cba.ca/technology-and-banking

预计到2018年，加拿大的电子商务销售预计将达到400亿加元。大约75％的加拿大人使用互联网银行。2显然，互联网和许多在线信息系统已成为加拿大关键基础设施的一部分。但是，统计数据还表明，加拿大人的个人，行业和政府并没有为新的网络威胁浪潮做好准备。深度学习是人工神经网络（ANN）的重大进步，它是一个受人脑处理信息如何启发的机器学习框架。由于高性能计算的改善，大量数据的可用性以及ANN，深度学习最近在多个领域表现出许多有希望的突破，从下棋到自动驾驶汽车。我们如何利用深度学习的进步来增强我们的网络空间和数据隐私的安全性？该研究计划的长期目标是增强网络安全性和隐私社区的大规模数据分析能力，以便安全专业人员可以有效地响应安全事件和数据保管人可以有效地保护其客户的数据隐私。在这项为期5年的研究计划中，我将重点介绍以下两个短期目标。第一个目标是增强网络安全专业人员对组装代码分析的深入学习能力，并为它们提供新一代的软件逆向工程方法和工具，以有效地了解良性软件和恶意软件二元组的内部工作。具体来说，团队将使用深度学习技术开发组装代码搜索引擎和描述生成器。申请人与公共和私营部门的网络安全专业人员密切合作；研究结果将直接提高加拿大网络空间的安全性。这些项目将是开源；因此，结果还将受益于软件反向工程和机器学习社区。第二个目标是为数据保管人或个人互联网用户提供释放数字文本文档的能力，例如产品评论，意见文章，博客等，而不会因其数字写作样式而损害作者的身份。可交付的将包括开源隐私的文本释义引擎。研究结果将增强社交媒体中用户的匿名性，这反过来促进了网络自由和反对社交媒体审查的斗争。研究结果还将在共享其管理的文本数据时为数据保管人提供添加的隐私保护层。这也将间接有助于开放数据运动。 1 www.pfsweb.com/blog/2016-canada-ecommerce-market2 www.cba.ca/technology-and-banking