Machine Learning for Improving Embedded System Attacks

用于改善嵌入式系统攻击的机器学习

• 批准号: RGPIN-2020-06175
• 负责人: OFlynn, Colin
• 金额: $ 2.04万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718257
• 关键词: Machine; Learning; Improving; Embedded; System

Embedded systems have become present in our daily life, from the many embedded computers involved in controlling a car, to implanted medical devices, to the latest Internet of Things (IoT) device. A common question (or what should be a common question) when using these devices has been questions around what security flaws might be present, and what attacks are possible to perform against these devices. While many attacks on these embedded computers are similar to well known attack from "classic" computing systems, there is a variety of attacks that are specific to embedded computers. In particular, the two most powerful classes of attacks are known as sidechannel power analysis and fault injection attacks. The first group (side channel power analysis) exploits fundamental artifacts of implementation of cryptography in the device under attack. The second group (fault injection attacks) exploits the ability of an attacker on an embedded system to alter the execution environment of the device being attacked, meaning that tasks such as verifying a signature can be manipulated. These attacks have been used in more recent attacks on embedded systems, including demonstrating a worm on the Philips Hue smart lights and bypassing security mechanisms on an automotive ECU. These attacks will be the prominent form of exploitation of embedded systems moving forward, as they allow breaking of state-of-theart cryptographic and security mechanisms. This research proposal will develop new tools and techniques, with the objective of not only developing the fundamental research, but disseminating this research to industry and academia through opensource tools. Specifically, several areas of research will be concentrated on towards this goal. The first will be to develop an interface between existing opensource sidechannel analysis frameworks (ChipWhisperer) and machine learning frameworks. This will be released as an opensource addition, and this framework will be used during the remaining research period. With this framework, optimization of machine learning attacks can be performed against a variety of target devices and cryptographic implementations. The second area of focus will be on fault injection using an electromagnetic fault injection (EMFI) platform. This will be split into use of machine learning for optimizing fault injection parameters to achieve a desired effect, and work on instrumenting a target device to better understand the effects that fault injection has on the target. A successful research proposal would build Canadian expertise in this critical area, including academic researchers, undergraduate engineers, and industry practitioners. This builds upon the principle investigators existing experience in this area, including his industry experience running workshops and training seminars.

嵌入式系统已经出现在我们的日常生活中，从控制汽车的许多嵌入式计算机到植入式医疗设备，再到最新的物联网（IoT）设备。在使用这些设备时，一个常见的问题（或者应该是一个常见的问题）是关于可能存在什么安全缺陷以及可能对这些设备执行什么攻击的问题。虽然对这些嵌入式计算机的许多攻击类似于来自“经典”计算系统的众所周知的攻击，但是存在各种特定于嵌入式计算机的攻击。 特别是，两种最强大的攻击类型被称为侧通道功率分析和故障注入攻击。第一组（侧信道功率分析）利用在受攻击的设备中实现密码学的基本工件。第二组（故障注入攻击）利用嵌入式系统上的攻击者改变被攻击设备的执行环境的能力，这意味着可以操纵诸如验证签名之类的任务。 这些攻击已被用于最近对嵌入式系统的攻击，包括演示飞利浦Hue智能灯上的蠕虫病毒和绕过汽车ECU上的安全机制。这些攻击将成为嵌入式系统的主要利用形式，因为它们允许破坏最先进的加密和安全机制。这项研究计划将开发新的工具和技术，其目标不仅是发展基础研究，而且通过开源工具将这项研究传播给工业界和学术界。 具体而言，将集中研究几个领域，以实现这一目标。首先是在现有的开源侧通道分析框架（ChipWhisperer）和机器学习框架之间开发一个接口。这将作为开源补充发布，该框架将在剩余的研究期间使用。通过这个框架，可以针对各种目标设备和加密实现执行机器学习攻击的优化。第二个重点领域将是使用电磁故障注入（EMFI）平台进行故障注入。这将分为使用机器学习来优化故障注入参数以实现预期效果，以及对目标设备进行仪表化以更好地了解故障注入对目标的影响。 一个成功的研究提案将建立加拿大在这一关键领域的专业知识，包括学术研究人员，本科工程师和行业从业者。这是建立在主要调查员在这一领域的现有经验，包括他的行业经验举办讲习班和培训研讨会。