Tree structure network: A learning-based deep network for classification of CPU instruction through em signal

Hao Yu Fang, Shih Yi Yuan, Po Yen Lin, Sy Yen Kuo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, we proposed a learning-based method to find the instruction which CPU is executing. Reverse engineering is an important issue on military, product security analysis, and intellectual property. Our work is a kind of reverse engineering. We can get the information of instruction without direct accessing CPU. While a CPU is running, it would emit EM signal. We collect the EM signal and combine Deep learning model and novel speech processing method to classify it. In our work, firstly, we use Deep learning network based on convolution neural network (CNN). Then, we propose a Tree Structure Network (TSN) and combine label to solve the problems of both data imbalance and Hard Separate Pairs(HSP). Finally, we can classify the EM signal with 61% Top-1 (the strictest) accuracy. To the best of our knowledge, this is the first work that use learning-based method to classify the EM signal.

Original languageEnglish
Title of host publication2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility, EMC Sapporo/APEMC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages246-249
Number of pages4
ISBN (Electronic)9784885523229
DOIs
StatePublished - 06 2019
Externally publishedYes
Event2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility, EMC Sapporo/APEMC 2019 - Sapporo, Japan
Duration: 03 06 201907 06 2019

Publication series

Name2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility, EMC Sapporo/APEMC 2019

Conference

Conference2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility, EMC Sapporo/APEMC 2019
Country/TerritoryJapan
CitySapporo
Period03/06/1907/06/19

Bibliographical note

Publisher Copyright:
© 2019 The Institute of Electronics, Information and Communication Engineer.

Keywords

  • Convolutional neural network
  • Information leakage
  • Instruction EM signal classification
  • Reverse engineering

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