THE CPA QUALIFICATION METHOD BASED ON THE GAUSSIAN CURVE FITTING

M.T. Adithia




Abstract


The Correlation Power Analysis (CPA) attack is an attack on cryptographic devices, especially smart cards. The results of the attack are correlation traces. Based on the correlation traces, an evaluation is done to observe whether significant peaks appear in the traces or not. The evaluation is done manually, by experts. If significant peaks appear then the smart card is not considered secure since it is assumed that the secret key is revealed. We develop a method that objectively detects peaks and decides which peak is significant. We conclude that using the Gaussian curve fitting method, the subjective qualification of the peak significance can be objectified. Thus, better decisions can be taken by security experts. We also conclude that the Gaussian curve fitting method is able to show the influence of peak sizes, especially the width and height, to a significance of a particular peak.


Keywords


Cryptography, side channel attack, correlation power analysis, smart cards, significant peak detection, Gaussian curve fitting

References


  1. Mangard, S., Oswald, E., and Popp, T., Power analysis attack: Revealing the secrets of smart cards, Springer, 2007.
  2. P.C. Kocher, J. Jaffe, and B. Jun, Differential power analysis, proceedings of Crypto 1999, Lecture notes in Computer Science, vol. 1666, pp. 398-412, 1999.
  3. E. Brier, C. Clavier, F. Olivier, Correlation power analysis with a leakage model, proceedings of CHES 2004, Lecture notes in Computer Science, vo. 3156, pp. 16-29, 2004.
  4. F.X. Standaert, T.G. Malkin, and M. Yung, A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks, Cryptology ePrint Archive, Report 2006/139.
  5. F.X. Standaert, T.G. Malkin, and M. Yung, A Formal Practice-Oriented Model for the Analysis of Side-Channel Attacks, Cryptology ePrint Archive, Report 2006/139, http://eprint.iacr.org/.
  6. F.X. Standaert, A Didactic Classification of some Illustrative Leakage Functions, in the proceedings of WISSEC 2006, Antwerp, Belgium, 2006.
  7. S.Q. Zhang, et al., Peak detection with chemical noise removal using short-time FFT for kind of MALDI data, the First International Symposium on Optimization and Systems Biology, Beijing, China, 2007.
  8. P. Du, W.A. Kibbe, and .S.M. Lin, Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching, Bioinformatics Advance Access, 2006.
  9. E. Lange, et al., High-accuracy peak picking of proteomics data using wavelet techniques, Pacific Symposium on Biocomputing 11, pp. 243-254, 2006.
  10. M. Dijkstra, et al., Peak quantification in surface-enhanced laser desorption/ionization by using mixture models, Proteomics, 2006.
  11. Statgraphics Centurion, Multivariate Methods, http://www.statgraphics.com/multivariate_methods.htm


Full Text: PDF

The Journal is published by The Institute of Research & Community Outreach - Petra Christian University. It available online supported by Directorate General of Higher Education - Ministry of National Education - Republic of Indonesia.

©All right reserved 2016.Jurnal Informatika, ISSN: 1411-0105

 

free hit counters
View My Stats




Copyright © Research Center Web-Dev Team