Rozvoj kryptoanalytických metod prostřednictvím evolučních výpočtů

English title

Advancing cryptanalytic methods through evolutionary computing

Type

grant

Keywords

cryptanalysis; cryptographic algorithm; distinguisher; security; evolutionary computing; optimization

Abstract

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?

Team members

Matyáš Václav, prof. RNDr., M.Sc., Ph.D. (FI MUNI) , research leader
Sekanina Lukáš, prof. Ing., Ph.D. (UPSY FIT VUT) , team leader
Dobai Roland, Ing., Ph.D. (UPSY FIT VUT)
Grochol David, Ing., Ph.D. (UPSY FIT VUT)

Publications

2019

MRÁZEK 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, vol. 27, no. 12, 2019, pp. 2734-2744. ISSN 1063-8210. Detail

2018

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. Detail
GROCHOL David and SEKANINA Lukáš. Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions. In: European Conference on Genetic Programming. Lecture Notes in Computer Science, vol. 10781. Berlin: Springer International Publishing, 2018, pp. 187-202. ISBN 978-3-319-77553-1. Detail

2017

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. Detail
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. Detail
GROCHOL 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. Detail

2016

DOBAI 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. Detail