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DeepSecurity - Applying Deep Learning to Hardware Security

DeepSecurity - 将深度学习应用于硬件安全

基本信息

批准号：
EP/R011494/1
负责人：
Máire O'Neill
金额：
$ 97.58万
依托单位：
Queen's University Belfast
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FR011494%2F1
关键词：
DeepSecurity Applying Deep Learning Hardware

项目摘要

With the globalisation of supply chains the design and manufacture of today's electronic devices are now distributed worldwide, for example, through the use of overseas foundries, third party intellectual property (IP) and third party test facilities. Many different untrusted entities may be involved in the design and assembly phases and therefore, it is becoming increasingly difficult to ensure the integrity and authenticity of devices. The supply chain is now considered to be susceptible to a range of hardware-based threats, including hardware Trojans, IP piracy, integrated circuit (IC) overproduction or recycling, reverse engineering, IC cloning and side-channel attacks. These attacks are major security threats to military, medical, government, transportation, and other critical and embedded systems applications. The proposed project will use a common approach to investigate two of these threats, namely the use of deep-learning in the context of side-channel attacks and hardware Trojans.Side-channel attacks (SCAs) exploit physical signal leakages, such as power consumption, electromagnetic emanations or timing characteristics, from cryptographic implementations, and have become a serious security concern with many practical real-world demonstrations, such as secret key recovery from the Mifare DESFire smart card used in public transport ticketing applications and from encrypted bitstreams on Xilinx Virtex-4/5 FPGAs. A hardware Trojan (HT) is a malicious modification of a circuit in order to control, modify, disable, monitor or affect the operation of the circuit. Although there have been no public reports of HTs detected in practice, in 2008 it was speculated that a critical failure in a Syrian radar may have been intentionally triggered via a hidden 'back door' inside a commercial off-the-shelf (COTS) microprocessor. The proposed project seeks to investigate the application of deep learning in SCA and HT detection, with the ultimate goal of utilising deep learning based verification processes in Electronic Design Automation tools to provide feedback to designers on the security of their designs. In relation to the call, the project addresses the challenge of 'maintaining confidence in security through the development process', and more specifically 'building supply chain confidence' and 'novel hardware analysis toolsets and techniques'.

随着供应链的全球化，今天的电子设备的设计和制造现在分布在世界各地，例如，通过使用海外代工厂，第三方知识产权（IP）和第三方测试设施。在设计和组装阶段可能涉及许多不同的不可信实体，因此，确保设备的完整性和真实性变得越来越困难。供应链现在被认为容易受到一系列基于硬件的威胁，包括硬件木马、IP盗版、集成电路（IC）生产过剩或回收、逆向工程、IC克隆和侧信道攻击。这些攻击是军事、医疗、政府、交通和其他关键和嵌入式系统应用程序的主要安全威胁。拟议的项目将使用一种常见的方法来调查其中的两种威胁，即在侧信道攻击和硬件木马的背景下使用深度学习。侧信道攻击（SCAs）利用加密实现中的物理信号泄漏，例如功耗，电磁辐射或定时特性，并且已经成为许多实际演示中严重的安全问题，例如从公共交通票证应用中使用的Mifare DESFire智能卡和Xilinx Virtex-4/5 fpga上的加密比特流中恢复密钥。硬件木马（HT）是一种恶意修改电路，以控制、修改、禁用、监视或影响电路的运行。尽管没有在实践中检测到HTs的公开报告，在2008年推测叙利亚雷达的一个关键故障可能已经通过商业现货（COTS）微处理器内部的隐藏“后门”被故意触发。拟议的项目旨在研究深度学习在SCA和HT检测中的应用，最终目标是在电子设计自动化工具中利用基于深度学习的验证过程，为设计人员提供有关其设计安全性的反馈。与该呼吁相关的是，该项目解决了“通过开发过程保持对安全的信心”的挑战，更具体地说，是“建立供应链信心”和“新颖的硬件分析工具集和技术”。