Smart cards are widely used in different areas of modern life including identification, banking, and transportation cards. Some types of cards are able to store data and process information as well. A number of them can run cryptographic algorithms to enhance the security of their transactions and it is usually believed that the information and values stored in them are completely safe. However, this is generally not the case due to the threat of the side channel. Side channel analysis is the process of obtaining additional information from the internal activity of a physical device beyond that allowed by its specifications. There exist different techniques to attempt to obtain information from a cryptosystem using other ways than the normally permitted. This thesis presents a series of experiments intended to study the side channel from a particular type of smart card, known as Java Cards. This investigation uses the well known technique, Correlation Analysis, and a new type of side channel attack called fast correlation in the frequency domain to study the side channel of Java Cards. This research presents a giant magnetoresistor (GMR) probe and for the first time, this type of sensor is used to investigate the side channel. A novel setup designed for studying the side channel of smart cards is described and two metrics used to evaluate the analysis results are presented. After testing the GMR probe and methodology on electronic devices executing the Advanced Encryption Standard (AES), such as 8 bit microcontrollers and 128 bit AES implementations on FPGAs, these techniques were applied to analyse two different models of Java Cards working in the contactless mode. The results show that successful attacks on a software implementation of AES running on both models of Java Cards are possible.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/6685 |
| Date | January 2012 |
| Creators | Mateos Santillan, Edgar |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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