Prof. Ing. Lukáš Sekanina, Ph.D.

Advancing cryptanalytic methods through evolutionary computing

Czech title:Rozvoj kryptoanalytických metod prostřednictvím evolučních výpočtů
Research leader:Matyáš Václav (FI MUNI)
Team leaders:Sekanina Lukáš
Team members:Dobai Roland, Grochol David
Agency:Czech Science Foundation
Keywords:cryptanalysis; cryptographic algorithm; distinguisher; security; evolutionary computing; optimization
Cryptographic algorithms usually go through elaborate testing by skilled experts who assert their overall security. We suggest to partly replace such extensive human labour by automating initial parts of such analyses. We base our approach on automatically generated "distinguishers" that show undesired statistical anomalies in an algorithm output. We design a distinguisher in the form of a multiple-output logic function, using evolutionary algorithms (genetic programming). We show that such approach leads to promising results comparable to the state-of-the-art testing. Our approach builds a distinguisher automatically and adaptively to the evaluated algorithm output. This opens up new possibilities for discovering those potential weaknesses in cryptographic algorithms that remained hidden from statistical tests and cryptanalysts sights. Our research will aim to answer two crucial questions of atmost importance when considering an algorithm security: (1) Is there anything wrong with a crypto algorithm? (2) What is wrong in the algorithm design? 


2019MRÁZEK Vojtěch, SEKANINA Lukáš, DOBAI Roland, SÝS Marek and ŠVENDA Petr. Efficient On-Chip Randomness Testing Utilizing Machine Learning Techniques. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 2019, vol. 99, no. 99, pp. 1-11. ISSN 1063-8210.
2018GROCHOL David and SEKANINA Lukáš. Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions. In: European Conference on Genetic Programming. Berlin: Springer International Publishing, 2018, pp. 187-202. ISBN 978-3-319-77553-1.
 MRÁZEK Vojtěch, SÝS Marek, VAŠÍČEK Zdeněk, SEKANINA Lukáš and MATYÁŠ Václav. Evolving Boolean Functions for Fast and Efficient Randomness Testing. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18). Kyoto: Association for Computing Machinery, 2018, pp. 1302-1309. ISBN 978-1-4503-5618-3.
2017GROCHOL David and SEKANINA Lukáš. Multiobjective Evolution of Hash Functions for High Speed Networks. In: Proceedings of the 2017 IEEE Congress on Evolutionary Computation. San Sebastian: IEEE Computer Society, 2017, pp. 1533-1540. ISBN 978-1-5090-4600-3.
 HUSA Jakub and DOBAI Roland. Designing Bent Boolean Functions With Parallelized Linear Genetic Programming. In: GECCO Companion '17 Proceedings of the Companion Publication of the 2017 on Genetic and Evolutionary Computation Conference. Berlín: Association for Computing Machinery, 2017, pp. 1825-1832. ISBN 978-1-4503-4939-0.
 KIDOŇ Marek and DOBAI Roland. Evolutionary design of hash functions for IP address hashing using genetic programming. In: 2017 IEEE Congress on Evolutionary Computation (CEC). San Sebastian: Institute of Electrical and Electronics Engineers, 2017, pp. 1720-1727. ISBN 978-1-5090-4601-0.
2016DOBAI Roland, KOŘENEK Jan and SEKANINA Lukáš. Adaptive Development of Hash Functions in FPGA-Based Network Routers. In: 2016 IEEE Symposium Series on Computational Intelligence. Athens: IEEE Computational Intelligence Society, 2016, pp. 1-8. ISBN 978-1-5090-4240-1.

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