A Multi-Layer Hardware Trojan Protection Framework for IoT Chips

被引:40
|
作者
Dong, Chen [1 ,2 ]
He, Guorong [1 ,2 ]
Liu, Ximeng [1 ,2 ]
Yang, Yang [1 ,2 ]
Guo, Wenzhong [1 ,3 ]
机构
[1] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Fujian, Peoples R China
[2] Fuzhou Univ, Key Lab Informat Secur Network Syst, Fuzhou 350116, Fujian, Peoples R China
[3] Fuzhou Univ, Fujian Prov Key Lab Network Comp & Intelligent In, Fuzhou 350116, Fujian, Peoples R China
基金
中国国家自然科学基金;
关键词
Internet of Things; protection framework; hardware security; hardware Trojan; GATE-LEVEL NETLISTS; SECURITY THREATS; CLASSIFICATION;
D O I
10.1109/ACCESS.2019.2896479
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Since integrated circuits are performed by several untrusted manufacturers, malicious circuits (hardware Trojans) can be implanted in any stage of the Internet-of-Things (IoT) devices. With the globalization of the IoT device manufacturing technologies, protecting the system-on-chip (SoC) security is always the keys issue for scientists and IC manufacturers. The existing SoC high-level synthesis approaches cannot guarantee both register-transfer-level and gate-level security, such as some formal verification and circuit characteristic analysis technologies. Based on the structural characteristics of hardware Trojans, we propose a multi-layer hardware Trojan protection framework for the Internet-of-Things perception layer called RG-Secure, which combines the third-party intellectual property trusted design strategy with the scan-chain netlist feature analysis technology. Especially at the gate level of chip design, our RG-Secure is equipped with a distributed, lightweight gradient lifting algorithm called lightGBM. The algorithm can quickly process high-dimensional circuit feature information and effectively improve the detection efficiency of hardware Trojans. In the meanwhile, a common evaluation index F-measure is used to prove the effectiveness of our method. The experiments show that RG-Secure framework can simultaneously detect register-transfer-level and gate-level hardware Trojans. For the trust-HUB benchmarks, the optimized lightGBM classifier achieves up to 100% true positive rate and 94% true negative rate; furthermore, it achieves 99.8% average F-measure and 99% accuracy, which shows a promising approach to ensure security during the design stage.
引用
收藏
页码:23628 / 23639
页数:12
相关论文
共 50 条
  • [41] Multi-layer LSTM Parallel Optimization Based on Hardware and Software Cooperation
    Chen, Qingfeng
    Wu, Jing
    Huang, Feihu
    Han, Yu
    Zhao, Qiming
    KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, PT II, 2022, 13369 : 681 - 693
  • [42] A Multi-Layer Parallel Hardware Architecture for Homomorphic Computation in Machine Learning
    Xin, Guozhu
    Zhao, Yifan
    Han, Jun
    2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2021,
  • [43] A Customized Hardware Architecture for Multi-layer Artificial Neural Networks on FPGA
    Huynh Minh Vu
    Huynh Viet Thang
    INFORMATION SYSTEMS DESIGN AND INTELLIGENT APPLICATIONS, INDIA 2017, 2018, 672 : 637 - 644
  • [44] Implementation of multi-layer neural network system for neuromorphic hardware architecture
    Sun, Wookyung
    Park, Junhee
    Jo, Sumin
    Lee, Jungwon
    Shin, Hyungsoon
    2019 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC), 2019, : 312 - 313
  • [45] Multi-layer PMMA microfluidic chips with channel networks for liquid sample operation
    Li, J. M.
    Liu, C.
    Liu, J. S.
    Xu, Z.
    Wang, L. D.
    JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2009, 209 (15-16) : 5487 - 5493
  • [46] Multi-layer atom chips for atom tunneling experiments near the chip surface
    Chuang, Ho-Chiao
    Salim, Evan A.
    Vuletic, Vladan
    Anderson, Dana Z.
    Bright, Victor M.
    SENSORS AND ACTUATORS A-PHYSICAL, 2011, 165 (01) : 101 - 106
  • [47] Multi-Layer and Clustering-Based Security Implementation for an IoT Environment
    Gupta, Deena Nath
    Kumar, Rajendra
    INTERNATIONAL JOURNAL OF SYSTEM DYNAMICS APPLICATIONS, 2022, 11 (02)
  • [48] A Review Paper on Machine Learning Based Trojan Detection in the IoT Chips
    Lavanya, T.
    Rajalakshmi, K.
    INTERNET OF THINGS AND CONNECTED TECHNOLOGIES, 2022, 340 : 225 - 238
  • [49] Debris Protection Capability Numerical Simulation of Multi-layer Insulation
    Zhou Guangdong
    Jia Guanghui
    PROCEEDINGS OF 2010 ASIA-PACIFIC INTERNATIONAL SYMPOSIUM ON AEROSPACE TECHNOLOGY, VOL 1 AND 2, 2010, : 765 - 768
  • [50] An SDN-enabled multi-layer protection and restoration mechanism
    Mirkhanzadeh, Behzad
    Shakeri, Ali
    Shao, Chencheng
    Razo, Miguel
    Tacca, Marco
    Galimberti, Gabriele Maria
    Martinelli, Giovanni
    Cardani, Marco
    Fumagalli, Andrea
    OPTICAL SWITCHING AND NETWORKING, 2018, 30 : 23 - 32