k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks
Power analysis presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications) and contact smart cards. In recent years, the cryptographic community has explored new approaches in power analysis based on machine learning models...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Spolecnost pro radioelektronicke inzenyrstvi
2016-06-01
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| Series: | Radioengineering |
| Subjects: | |
| Online Access: | http://www.radioeng.cz/fulltexts/2016/16_02_0365_0382.pdf |
| _version_ | 1818844809926803456 |
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| author | Z. Martinasek V. Zeman L. Malina J. Martinasek |
| author_facet | Z. Martinasek V. Zeman L. Malina J. Martinasek |
| author_sort | Z. Martinasek |
| collection | DOAJ |
| description | Power analysis presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications) and contact smart cards. In recent years, the cryptographic community has explored new approaches in power analysis based on machine learning models such as Support Vector Machine (SVM), RF (Random Forest) and Multi-Layer Perceptron (MLP). In this paper, we made an extensive comparison of machine learning algorithms in the power analysis. For this purpose, we implemented a verification program that always chooses the optimal settings of individual machine learning models in order to obtain the best classification accuracy. In our research, we used three datasets, the first containing the power traces of an unprotected AES (Advanced Encryption Standard) implementation. The second and third datasets are created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4). The obtained results revealed some interesting facts, namely, an elementary k-NN (k-Nearest Neighbors) algorithm, which has not been commonly used in power analysis yet, shows great application potential in practice. |
| first_indexed | 2024-12-19T05:19:40Z |
| format | Article |
| id | doaj.art-4efee2b7bf0249a09e717ef943328ed3 |
| institution | Directory Open Access Journal |
| issn | 1210-2512 |
| language | English |
| last_indexed | 2024-12-19T05:19:40Z |
| publishDate | 2016-06-01 |
| publisher | Spolecnost pro radioelektronicke inzenyrstvi |
| record_format | Article |
| series | Radioengineering |
| spelling | doaj.art-4efee2b7bf0249a09e717ef943328ed32022-12-21T20:34:32ZengSpolecnost pro radioelektronicke inzenyrstviRadioengineering1210-25122016-06-01252365382k-Nearest Neighbors Algorithm in Profiling Power Analysis AttacksZ. MartinasekV. ZemanL. MalinaJ. MartinasekPower analysis presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications) and contact smart cards. In recent years, the cryptographic community has explored new approaches in power analysis based on machine learning models such as Support Vector Machine (SVM), RF (Random Forest) and Multi-Layer Perceptron (MLP). In this paper, we made an extensive comparison of machine learning algorithms in the power analysis. For this purpose, we implemented a verification program that always chooses the optimal settings of individual machine learning models in order to obtain the best classification accuracy. In our research, we used three datasets, the first containing the power traces of an unprotected AES (Advanced Encryption Standard) implementation. The second and third datasets are created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4). The obtained results revealed some interesting facts, namely, an elementary k-NN (k-Nearest Neighbors) algorithm, which has not been commonly used in power analysis yet, shows great application potential in practice.http://www.radioeng.cz/fulltexts/2016/16_02_0365_0382.pdfPower AnalysisMachine LearningTemplate AttackComparisonSmart Cards |
| spellingShingle | Z. Martinasek V. Zeman L. Malina J. Martinasek k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks Radioengineering Power Analysis Machine Learning Template Attack Comparison Smart Cards |
| title | k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks |
| title_full | k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks |
| title_fullStr | k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks |
| title_full_unstemmed | k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks |
| title_short | k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks |
| title_sort | k nearest neighbors algorithm in profiling power analysis attacks |
| topic | Power Analysis Machine Learning Template Attack Comparison Smart Cards |
| url | http://www.radioeng.cz/fulltexts/2016/16_02_0365_0382.pdf |
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