• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Design, Automation Test in Europe Conference Exhibition >Power profiling of microcontroller's instruction set for runtime hardware Trojans detection without golden circuit models
【24h】

Power profiling of microcontroller's instruction set for runtime hardware Trojans detection without golden circuit models

机译：微控制器指令集的功耗分析，用于运行时硬件木马检测，无需黄金电路模型

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Globalization trends in integrated circuit (IC) design are leading to increased vulnerability of ICs against hardware Trojans (HT). Recently, several side channel parameters based techniques have been developed to detect these hardware Trojans that require golden circuit as a reference model, but due to the widespread usage of IPs, most of the system-on-chip (SoC) do not have a golden reference. Hardware Trojans in intellectual property (IP)-based SoC designs are considered as major concern for future integrated circuits. Most of the state-of-the-art runtime hardware Trojan detection techniques presume that Trojans will lead to anomaly in the SoC integration units. In this paper, we argue that an intelligent intruder may intrude the IP-based SoC without disturbing the normal SoC operation or violating any protocols. To overcome this limitation, we propose a methodology to extract the power profile of the micro-controllers instruction sets, which is in turn used to train a machine learning algorithm. In this technique, the power profile is obtained by extracting the power behavior of the micro-controllers for different assembly language instructions. This trained model is then embedded into the integrated circuits at the SoC integration level, which classifies the power profile during runtime to detect the intrusions. We applied our proposed technique on MC8051 micro-controller in VHDL, obtained the power profile of its instruction set and then applied deep learning, k-NN, decision tree and naive Bayesian based machine learning tools to train the models. The cross validation comparison of these learning algorithm, when applied to MC8051 Trojan benchmarks, shows that we can achieve 87% to 99% accuracy. To the best of our knowledge, this is the first work in which the power profile of a microprocessor's instruction set is used in conjunction with machine learning for runtime HT detection.
机译：集成电路（IC）设计的全球化趋势导致IC对硬件特洛伊木马（HT）的脆弱性增加。最近，已经开发了几种基于边信道参数的技术来检测需要黄金电路作为参考模型的这些硬件木马，但是由于IP的广泛使用，大多数片上系统（SoC）都没有黄金参考。基于知识产权（IP）的SoC设计中的硬件木马被认为是未来集成电路的主要关注点。大多数最新的运行时硬件Trojan检测技术都假定Trojans会导致SoC集成单元出现异常。在本文中，我们认为智能入侵者可以入侵基于IP的SoC，而不会干扰正常的SoC操作或违反任何协议。为了克服此限制，我们提出了一种方法来提取微控制器指令集的功率曲线，然后将其用于训练机器学习算法。在该技术中，通过提取针对不同汇编语言指令的微控制器的电源行为来获得电源配置文件。然后，将这种经过训练的模型以SoC集成级别嵌入到集成电路中，该模型在运行时对功率分布进行分类以检测入侵。我们在VHDL中的MC8051单片机上应用了我们提出的技术，获得了其指令集的功率曲线，然后应用了基于深度学习，k-NN，决策树和朴素贝叶斯的机器学习工具来训练模型。将这些学习算法应用于MC8051 Trojan基准进行交叉验证比较，表明我们可以达到87％至99％的精度。据我们所知，这是第一项将微处理器指令集的功率曲线与机器学习结合使用以进行运行时HT检测的工作。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号