Side-channel attacks and machine learning approach
Most modern devices and cryptoalgorithms are vulnerable to a new class of attack called side-channel attack. It analyses physical parameters of the system in order to get secret key. Most spread techniques are simple and differential power attacks with combination of statistical tools. Few studies c...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
FRUCT
2016-04-01
|
| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://fruct.org/publications/fruct18/files/Lev.pdf
|
| _version_ | 1819163853335822336 |
|---|---|
| author | Alia Levina Daria Sleptsova Oleg Zaitsev |
| author_facet | Alia Levina Daria Sleptsova Oleg Zaitsev |
| author_sort | Alia Levina |
| collection | DOAJ |
| description | Most modern devices and cryptoalgorithms are vulnerable to a new class of attack called side-channel attack. It analyses physical parameters of the system in order to get secret key. Most spread techniques are simple and differential power attacks with combination of statistical tools. Few studies cover using machine learning methods for pre-processing and key classification tasks. In this paper, we investigate applicability of machine learning methods and their characteristic. Following theoretical results, we examine power traces of AES encryption with Support Vector Machines algorithm and decision trees and provide roadmap for further research. |
| first_indexed | 2024-12-22T17:50:44Z |
| format | Article |
| id | doaj.art-67eb2c825f574cfa9ce49d253b75ff30 |
| institution | Directory Open Access Journal |
| issn | 2305-7254 2343-0737 |
| language | English |
| last_indexed | 2024-12-22T17:50:44Z |
| publishDate | 2016-04-01 |
| publisher | FRUCT |
| record_format | Article |
| series | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| spelling | doaj.art-67eb2c825f574cfa9ce49d253b75ff302022-12-21T18:18:10ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372016-04-016641818118610.1109/FRUCT-ISPIT.2016.7561525Side-channel attacks and machine learning approachAlia Levina0Daria Sleptsova1Oleg Zaitsev2ITMO University, Saint Petersburg, RussiaITMO University, Saint Petersburg, RussiaITMO University, Saint Petersburg, RussiaMost modern devices and cryptoalgorithms are vulnerable to a new class of attack called side-channel attack. It analyses physical parameters of the system in order to get secret key. Most spread techniques are simple and differential power attacks with combination of statistical tools. Few studies cover using machine learning methods for pre-processing and key classification tasks. In this paper, we investigate applicability of machine learning methods and their characteristic. Following theoretical results, we examine power traces of AES encryption with Support Vector Machines algorithm and decision trees and provide roadmap for further research.https://fruct.org/publications/fruct18/files/Lev.pdf Side-channel attacksAESMachine learning |
| spellingShingle | Alia Levina Daria Sleptsova Oleg Zaitsev Side-channel attacks and machine learning approach Proceedings of the XXth Conference of Open Innovations Association FRUCT Side-channel attacks AES Machine learning |
| title | Side-channel attacks and machine learning approach |
| title_full | Side-channel attacks and machine learning approach |
| title_fullStr | Side-channel attacks and machine learning approach |
| title_full_unstemmed | Side-channel attacks and machine learning approach |
| title_short | Side-channel attacks and machine learning approach |
| title_sort | side channel attacks and machine learning approach |
| topic | Side-channel attacks AES Machine learning |
| url | https://fruct.org/publications/fruct18/files/Lev.pdf
|
| work_keys_str_mv | AT alialevina sidechannelattacksandmachinelearningapproach AT dariasleptsova sidechannelattacksandmachinelearningapproach AT olegzaitsev sidechannelattacksandmachinelearningapproach |