An Electromagnetic Approach to Smart Card Instruction Identification using Machine Learning Techniques

被引：0

作者：

Tsague, Hippolyte Djonon ^{[1
]}

Twala, Bheki ^{[2
]}

机构：

[1] SIR, MDS, Smart Token Res Grp, Pretoria, South Africa

[2] Univ Johannesburg, Fac Engn, Inst Intelligent Syst, Dept Elect & Elect Engn Sci, Johannesburg, South Africa

来源：

2017 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTED, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI) | 2017年

关键词：

Side Channel Leakage; Electromagnetic Templates; Principal Components Analysis; Linear Discriminant Analysis; Multivariate Gaussian Distribution; k-Nearest Neighbours Algorithm; Reverse Engineering; POWER;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Since the first publication, side channel leakage has been widely used for the purposes of extracting secret information, such as cryptographic keys, from embedded devices. However, in a few instances it has been utilized for extracting other information about the internal state of a computing device. In this paper, we show how to create a robust instruction-level side channel leakage profile of an embedded processor. Using the electromagnetic profile we show how to extract executed instructions from a smart card's leakage with good accuracy. In addition, we provide a comparison between several performance and recognition enhancement tools.

引用

页数：8

共 50 条

[21] Automatic Device Identification and Anomaly Detection with Machine Learning Techniques in Smart Factories
Tien, Chin-Wei
Huang, Tse-Yung
Chen, Ping Chun
Wang, Jenq-Haur
2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, : 3539 - 3544
[22] Smart Fruit Identification and Counting using Machine Vision Approach
Shankarpure, Madhura R.
Patil, Dipti D.
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (12) : 283 - 289
[23] Smart Health Monitoring System using IOT and Machine Learning Techniques
Pandey, Honey
Prabha, S.
2020 SIXTH INTERNATIONAL CONFERENCE ON BIO SIGNALS, IMAGES, AND INSTRUMENTATION (ICBSII), 2020,
[24] Fraud Prediction in Smart Supply Chains Using Machine Learning Techniques
Constante-Nicolalde, Fabian-Vinicio
Guerra-Teran, Paulo
Perez-Medina, Jorge-Luis
APPLIED TECHNOLOGIES (ICAT 2019), PT II, 2020, 1194 : 145 - 159
[25] Detection of Sources of Instability in Smart Grids Using Machine Learning Techniques
Moldovan, Dorin
Salomie, Ioan
2019 IEEE 15TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING (ICCP 2019), 2019, : 175 - 182
[26] Language identification of character images using machine learning techniques
Liu, YH
Lin, CC
Chang, F
EIGHTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS 1 AND 2, PROCEEDINGS, 2005, : 630 - 634
[27] Automatic Identification of Honeypot Server Using Machine Learning Techniques
Huang, Cheng
Han, Jiaxuan
Zhang, Xing
Liu, Jiayong
SECURITY AND COMMUNICATION NETWORKS, 2019, 2019
[28] Hyperspectral identification of oil adulteration using machine learning techniques
Aqeel, Muhammad
Sohaib, Ahmad
Iqbal, Muhammad
Rehman, Hafeez Ur
Rustam, Furqan
CURRENT RESEARCH IN FOOD SCIENCE, 2024, 8
[29] Breast Cancer Subtype Identification Using Machine Learning Techniques
Firoozbakht, Forough
Rezaeian, Iman
Porter, Lisa
Rueda, Luis
2014 IEEE 4TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL ADVANCES IN BIO AND MEDICAL SCIENCES (ICCABS), 2014,
[30] Identification of nonlinear dynamic system using machine learning techniques
Samal D.
Bisoi R.
Sahu B.
International Journal of Power and Energy Conversion, 2021, 12 (01) : 23 - 43

← 1 2 3 4 5 →