Side Channels - The application of advanced techniques for system identification to study side channels for cryptographic algorithms: cryptanalysis and design
From 01-01-2003 to 31-12-2006
Description
During the last year, researchers are starting to realize that the security of a cryptographic system does not only depend on the mathematical analysis of the algorithm itself, but also on the security of the implementation. For example, in a number of concrete implementations side channels can be identified (such as the time it takes to execute an operation the power consumption signal during the execution or electromagnetic signals). If the cryptanalyst has access to such a side channel this may render the most secure algorithm completely insecure.
The goal of this project is to combine two previously unrelated disciplines. Finding a secret key (or a secret algorithm) corresponds to an identification problem, and advanced techniques from system identification or modeling should allow to improve over existing attacks. It is clear that these attacks can be improved substantially by exploiting information in multiple side channels simultaneously, for example, time, power consumption, and electromagnetic signals observed by multiple antennas. The system can then be modeled as a MIMO (Multiple Input Multiple Output) system. The most important factors that need to be taken into account are the signal to noise ratio, heteroscadisctic variations of the measurements and the incorporation of a priori information.
The following ideas from system identification and signal processing will be explored:
- generalized correlation-analysis;
- system identification. System identification techniques calculate a mathematical model of the cryptographic system from input/output data.
- advanced pattern recognition techniques, such as Least Squares Support Vector Machines (LS-SVMs), can be used to identify different instructions of a cryptographic algorithm.
- higher order statistics and multilinear algebra;
- Independent Component Analysis (ICA)
Team
- Bart Preneel, Co-promoter (External)
- Bart De Moor, Promoter
Financing
Funding: FWO - Research Foundation - Flanders
Program/Grant Type: FWO Research Grant - FWO Research Grant
Events
2/09/2024:
PhD defense - Martijn Oldenhof
Machine Learning for Advanced Chemical Analysis and Structure Recognition in Drug Discovery
3/09/2024:
Meet the Jury Igor Tetko on Advanced Machine Learning in Drug Discovery
12/09/2024:
Multimodal analysis of cell-free DNA for sensitive cancer detection in low-coverage and low-sample settings
Seminar by Antoine Passemiers
News
STADIUS Alumni Herman Verrelst – new CEO of Biocartis
08 June 2017
Herman Verrelst, the founder of KU Leuven spin-off Cartagenia, who has been working in Silicon Valley, US for the last few years will be returning to Belgium to follow the steps of Rudi Pauwels as CEO of the Belgian diagnostic company, Biocartis.
Supporting healthcare policymaking via machine learning – batteries included!
29 May 2017
STADIUS takes the lead in the data analytics efforts in an ambitious European Project MIDAS.
Marc Claesen gives an interview about his PhD for the magazine of the Faculty of Engineering Sciences "Geniaal"
10 February 2017
Did you know that in Belgium approximately one third of type 2 diabetes patients are unaware of their condition?
Joos Vandewalle is nieuwe voorzitter KVAB
09 October 2016
Op 5 oktober 2016 heeft de Algemene Vergadering van de Academie KVAB Joos Vandewalle verkozen tot voorzitter van de KVAB.