Machine learning techniques to predict sensitive patterns to fault attack in the Java Card application

References

• Aidemark, J., Vinter, J., Folkesson, P., & Karlsson, J. (2001). Goofi: Generic object-oriented fault injection tool. In Proceeding of the dependable systems and networks (pp. 83–88). Gothenburg, Sweden.
   Google Scholar
• Arlat, J. (1990). Validation de la sûreté de fonctionnement par injection de fautes, méthode- mise en oeuvre- application (Unpublished doctoral dissertation).
   Google Scholar
• Armstrong, D. B. (1972). A deductive method for simulating faults in logic circuits. IEEE Transactions on Computers, 21(5), 464–471.
   Web of Science ®Google Scholar
• Blomer, J., Otto, M., & Seifert, J. P. (2003). A new CRT - RSA algorithm secure against bellcore attacks. In Proceeding of the 10th ACM conference on computer and communications security (pp. 311–320), Washington D.C., USA.
   Google Scholar
• Boser, B., Guyon, I., & Vapnik, V. (1992). A training algorithm for optimal margin classifiers. In Proceeding of the fourth workshop on computational learning theory (pp. 144–152), New York, NY.
   Google Scholar
• Carreira, J., Madeira, H., Silva, J. G. (1998). Xception: Software fault injection and monitoring in processor functional units. Dependable Computing and Fault Tolerant Systems, 10, 245–266.
   Google Scholar
• Chang, C., & Lin, C. (2001). Libsvm: A library for support vector machines. Retrieved from http://www.csie.ntu.edu.tw/\~cjlin/libsvm
   Google Scholar
• Chawla, N., Bowyer, K., Hall, L., & Kegelmeyer, W. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
   Web of Science ®Google Scholar
• Civera, P., Macchiarulo, L., Rebaudengo, M., Reorda, M. S., & Violante, A. (2001). Exploiting fpga for accelerating fault injection experiments. In Proceedings of the seventh international on-line testing workshop, 2001 (pp. 9–13), Taormina, Italy.
   Google Scholar
• Dureuil, L., Potet, M.-L., de Choudens, P., Dumas, C., & Clédière, J. (2015). From code review to fault injection attacks: Filling the gap using fault model inference. In International conference on smart card research and advanced applications (pp. 107–124). Bochum, Germany.
   Google Scholar
• Freund, Y., & Schapire, R. (1996). Experiments with a new boosting algorithm. In Proceeding of the international conference on machine learning (pp. 148–156). Bari, Italy.
   Google Scholar
• Han, S., Shin, K. G., & Rosenberg, H. A. (1995). Doctor: An integrated software fault injection environment for distributed real-time systems. In Proceedings of the international computer performance and dependability symposium (pp. 204–213). Erlangen, Germany.
   Google Scholar
• Hardie, F. H., & Suhocki, R. J. (1967). Design and use of fault simulation for saturn computer design. IEEE Transactions on Electronic Computers, 4, 412–429.
   Google Scholar
• Hastie, T., & Tibshirani, R. (1998). Classification by pairwise coupling. Annals of Statistics, 26(2), 451–471.
   Web of Science ®Google Scholar
• He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.
   Web of Science ®Google Scholar
• Kanawati, G. A., Kanawati, N. A., & Abraham, J. A. (1995). Ferrari: A flexible software-based fault and error injection system. IEEE Transactions on computers, 44(2), 248–260.
   Web of Science ®Google Scholar
• Kauffmann-Tourkestansky, X. (2012). Analyses sécuritaires de code de carte à puce sous attaques physiques simulées. (Unpublished doctoral dissertation). Universit\’e d’Orl\’eans.
   Google Scholar
• Laurikkala, J. (2001). Improving identification of difficult small classes by balancing class distribution. In Proceedings of the 8th conference on AI in medicine in Europe: Artificial intelligence medicine AIME ’01 (pp. 63–66). London: Springer-Verlag.
   Google Scholar
• Leopold, E., & Kindermann, J. (2002). Text categorization with support vector machines. how to represent texts in input space? Machine Learning, 46(1–3), 423–444.
   Web of Science ®Google Scholar
• Leveugle, R. (2000). Fault injection in vhdl descriptions and emulation. In Proceedings of the IEEE international symposium on defect and fault tolerance in VLSI systems (pp. 414–419), Yamanashi, Japan.
   Google Scholar
• Machemie, J. B., Lanet, J. L., Bouffard, G., Poichotte, J. Y., & Wary, J. P. (2011). Evaluation of the ability to transform sim applications into hostile applications. In Proceeding of smart card research and advanced applications (pp. 1–17), Leuven, Belgium.
   Google Scholar
• Machemie, J. B., Mazin, C., Lanet, J. L., & Cartigny, J. (2011). SmartCM a smart card fault injection simulator. In IEEE international workshop on information forensics and security (pp. 1–6). Iguacu Falls, Brazil.
   Google Scholar
• Moulin, H., Bihame, E., & Shamir, A. (1997). Differential fault analysis of secret key cryptosystem. In Proceeding of Springer advances in cryptology (pp. 513–525), Santa Barbara, California, USA.
   Google Scholar
• Quinlan, J. R. (1993). C45: Programs for machine learning. San Francisco, CA, USA: Morgan Kaufmann.
   Google Scholar
• Rifkin, R., & Klautau, A. (2004). In defense of one-vs-all classification. Journal of Machine Learning Research, 5, 101–141.
   Web of Science ®Google Scholar
• Riviere, L., Bringer, J., Le, T.-H., & Chabanne, H. (2015). A novel simulation approach for fault injection resistance evaluation on smart cards. In IEEE 8th international conference on software testing verification and validation workshops ICSTW (pp. 1–8). Graz, Austria.
   Google Scholar
• Salton, G., Wang, A., & Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613–620.
   Web of Science ®Google Scholar
• Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1–47.
   Web of Science ®Google Scholar
• Sere, A. A., Iguchi-Cartigny, J., & Lanet, J.-L. (2009). Automatic detection of fault attack and countermeasures. In Proceedings of the 4th workshop on embedded systems security WESS (pp. 7:1–7:7). New York, NY: ACM.
   Google Scholar
• Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd ed.). San Francisco, CA: Morgan Kaufmann.
   Google Scholar
• Woods, K., Doss, C., Bowyer, K., Solka, J., Priebe, C., & Kegelmeyer, W. (1993). Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography. International Journal of Pattern Recognition and Artificial Intelligence, 7(6), 1417–1436.
   Google Scholar
• Yahiaoui, C., Lanet, J. L., Mezghiche, M., & Tamine, K. (2013). Recognition of sensitive patterns to the fault attack in the Java Card application. In Proceeding of 2nd conference on theoretical and applicative aspects of computer science (pp. 1–12), Skikda, Algeria.
   Google Scholar
• Yang, Y., & Pedersen, J. (1997). A comparative study on feature selection in text categorization. In Proceeding of the international conference on machine learning (pp. 412–420), Nashville, TN, USA.
   Google Scholar