Design of Convolutional Neural Networks Architecture for Non-Profiled Side-Channel Attack Detection
Deep learning (DL) is a new option that has just been made available for side-channel analysis. DL approaches for profiled side-channel attacks (SCA) have dominated research till now. In this attack, the attacker has complete control over the profiling device and can collect many traces for a range...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Kaunas University of Technology
2023-08-01
|
| Series: | Elektronika ir Elektrotechnika |
| Subjects: | |
| Online Access: | https://eejournal.ktu.lt/index.php/elt/article/view/33995 |
| _version_ | 1797608465690001408 |
|---|---|
| author | Amjed Abbas Ahmed Mohammad Kamrul Hasan Shayla Islam Azana Hafizah Mohd Aman Nurhizam Safie |
| author_facet | Amjed Abbas Ahmed Mohammad Kamrul Hasan Shayla Islam Azana Hafizah Mohd Aman Nurhizam Safie |
| author_sort | Amjed Abbas Ahmed |
| collection | DOAJ |
| description | Deep learning (DL) is a new option that has just been made available for side-channel analysis. DL approaches for profiled side-channel attacks (SCA) have dominated research till now. In this attack, the attacker has complete control over the profiling device and can collect many traces for a range of critical parameters to characterise device leakage before the attack. In this study, we apply DL algorithms to non-profiled data. An attacker can only retrieve a limited number of side-channel traces from a closed device with an unknown key value in non-profiled mode. The authors conducted this research. Key estimations and deep learning measurements can reveal the secret key. We prove that this is doable. This technology is excellent for non-profits. DL and neural networks can benefit these organisations. Neural networks can provide a new technique to verify the safety of hardware cryptographic algorithms. It was recently suggested. This study creates a non-profiled SCA utilising convolutional neural networks (CNNs) on an AVR microcontroller with 8 bits of memory and the AES-128 cryptographic algorithm. We used aligned power traces with several samples to demonstrate how challenging CNN-based SCA is in practise. This will help us reach our goals. Here is another technique to create a solid CNN data set. In particular, CNN-based SCA experiment data and noise effects are examined. These experiments employ power traces with Gaussian noise. The CNN-based SCA works well with our data set for non-profiled attacks. Gaussian noise on power traces causes many more issues. These results show that our method can recover more bytes successfully from SCA compared to other methods in correlation power analysis (CPA) and DL-SCA without regularisation. |
| first_indexed | 2024-03-11T05:44:47Z |
| format | Article |
| id | doaj.art-710a0956b14c4a1789d1ac9ed4be8346 |
| institution | Directory Open Access Journal |
| issn | 1392-1215 2029-5731 |
| language | English |
| last_indexed | 2024-03-11T05:44:47Z |
| publishDate | 2023-08-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj.art-710a0956b14c4a1789d1ac9ed4be83462023-11-17T15:02:10ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312023-08-01294768110.5755/j02.eie.3399539249Design of Convolutional Neural Networks Architecture for Non-Profiled Side-Channel Attack DetectionAmjed Abbas Ahmed0Mohammad Kamrul Hasan1Shayla Islam2Azana Hafizah Mohd Aman3Nurhizam Safie4Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, MalaysiaCenter for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, MalaysiaInstitute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, MalaysiaCenter for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, MalaysiaCenter for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, MalaysiaDeep learning (DL) is a new option that has just been made available for side-channel analysis. DL approaches for profiled side-channel attacks (SCA) have dominated research till now. In this attack, the attacker has complete control over the profiling device and can collect many traces for a range of critical parameters to characterise device leakage before the attack. In this study, we apply DL algorithms to non-profiled data. An attacker can only retrieve a limited number of side-channel traces from a closed device with an unknown key value in non-profiled mode. The authors conducted this research. Key estimations and deep learning measurements can reveal the secret key. We prove that this is doable. This technology is excellent for non-profits. DL and neural networks can benefit these organisations. Neural networks can provide a new technique to verify the safety of hardware cryptographic algorithms. It was recently suggested. This study creates a non-profiled SCA utilising convolutional neural networks (CNNs) on an AVR microcontroller with 8 bits of memory and the AES-128 cryptographic algorithm. We used aligned power traces with several samples to demonstrate how challenging CNN-based SCA is in practise. This will help us reach our goals. Here is another technique to create a solid CNN data set. In particular, CNN-based SCA experiment data and noise effects are examined. These experiments employ power traces with Gaussian noise. The CNN-based SCA works well with our data set for non-profiled attacks. Gaussian noise on power traces causes many more issues. These results show that our method can recover more bytes successfully from SCA compared to other methods in correlation power analysis (CPA) and DL-SCA without regularisation.https://eejournal.ktu.lt/index.php/elt/article/view/33995non-profile side-channel attackaescnn |
| spellingShingle | Amjed Abbas Ahmed Mohammad Kamrul Hasan Shayla Islam Azana Hafizah Mohd Aman Nurhizam Safie Design of Convolutional Neural Networks Architecture for Non-Profiled Side-Channel Attack Detection Elektronika ir Elektrotechnika non-profile side-channel attack aes cnn |
| title | Design of Convolutional Neural Networks Architecture for Non-Profiled Side-Channel Attack Detection |
| title_full | Design of Convolutional Neural Networks Architecture for Non-Profiled Side-Channel Attack Detection |
| title_fullStr | Design of Convolutional Neural Networks Architecture for Non-Profiled Side-Channel Attack Detection |
| title_full_unstemmed | Design of Convolutional Neural Networks Architecture for Non-Profiled Side-Channel Attack Detection |
| title_short | Design of Convolutional Neural Networks Architecture for Non-Profiled Side-Channel Attack Detection |
| title_sort | design of convolutional neural networks architecture for non profiled side channel attack detection |
| topic | non-profile side-channel attack aes cnn |
| url | https://eejournal.ktu.lt/index.php/elt/article/view/33995 |
| work_keys_str_mv | AT amjedabbasahmed designofconvolutionalneuralnetworksarchitecturefornonprofiledsidechannelattackdetection AT mohammadkamrulhasan designofconvolutionalneuralnetworksarchitecturefornonprofiledsidechannelattackdetection AT shaylaislam designofconvolutionalneuralnetworksarchitecturefornonprofiledsidechannelattackdetection AT azanahafizahmohdaman designofconvolutionalneuralnetworksarchitecturefornonprofiledsidechannelattackdetection AT nurhizamsafie designofconvolutionalneuralnetworksarchitecturefornonprofiledsidechannelattackdetection |