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Hardware Trojan Detection Using Deep Learning Technique

  • K. ReshmaEmail author
  • M. Priyatharishini
  • M. Nirmala Devi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)

Abstract

A method to detect hardware Trojan in gate-level netlist is proposed using deep learning technique. The paper shows that it is easy to identify genuine nodes and Trojan-infected nodes based on controllability and transition probability values of a given Trojan-infected circuit. The controllability and transition probability characteristics of Trojan-infected nodes show large inter-cluster distance from the genuine nodes so that it is easy to cluster the nodes as Trojan-infected nodes and genuine nodes. From a given circuit, controllability and transition probability values are extracted as Trojan features using deep learning algorithm and clustering the data using k-means clustering. The technique is validated on ISCAS’85 benchmark circuits, and it does not require any golden model as reference. The proposed method can detect all Trojan-infected nodes in less than 6 s with zero false positive and zero false negative detection accuracy.

Keywords

Controllability Deep learning Hardware Trojan Transition probability 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • K. Reshma
    • 1
    Email author
  • M. Priyatharishini
    • 1
  • M. Nirmala Devi
    • 1
  1. 1.Department of Electronics and Communication Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

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