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A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection.

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Summary
This summary is machine-generated.

This study introduces MHTtext, a deep learning model for detecting hardware Trojans in integrated circuits. It offers flexible strategies to balance accuracy and computational cost, improving security in cyber-physical systems and the Metaverse.

Keywords:
computational consumptiondeep learninggate levelhardware Trojanintegrated circuit securitysemantic analysis

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Cybersecurity

Background:

  • Cyber-physical systems and the Metaverse face increasing hardware security threats, particularly hardware Trojans in integrated circuits.
  • Existing hardware Trojan detection methods struggle with large-scale integration due to limitations like golden chips and high computational demands.
  • Traditional machine learning methods for hardware Trojan detection are often unstable due to difficulties in manual feature extraction.

Purpose of the Study:

  • To propose a novel deep learning-based multiscale detection model, MHTtext, for automatic hardware Trojan feature extraction and identification.
  • To develop strategies within MHTtext that balance detection accuracy with computational efficiency for practical applications.
  • To introduce a new evaluation metric, the stabilization efficiency index (SEI), for assessing the model's performance and stability.

Main Methods:

  • The MHTtext model employs deep learning for automatic feature extraction from netlist data.
  • Two distinct strategies (global and local) are implemented to cater to different accuracy and computational requirements.
  • TextCNN is utilized for hardware Trojan identification, with mechanisms to ensure non-repeated component information for enhanced stability.

Main Results:

  • The global strategy of MHTtext achieved an average accuracy of 99.26% in detecting hardware Trojans on benchmark netlists.
  • The MHTtext model demonstrated high stability and flexibility, with one strategy ranking first in SEI among comparison classifiers.
  • The local strategy also yielded excellent results, demonstrating the model's effectiveness across different operational modes.

Conclusions:

  • The proposed MHTtext model offers a stable, flexible, and accurate solution for hardware Trojan detection in large-scale integrated circuits.
  • The dual-strategy approach allows for adaptable performance based on specific application needs, addressing limitations of traditional methods.
  • MHTtext contributes to enhancing the security of critical hardware components within evolving digital environments like the Metaverse.