Project Details

Rozvoj kryptoanalytických metod prostřednictvím evolučních výpočtů

Project Period: 1. 1. 2016 – 31. 12. 2018

Project Type: grant

Code: GA16-08565S

Agency: Czech Science Foundation

Program: Standardní projekty

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, Dr. (CM-SFE) – research leader
Dobai Roland, Ing., Ph.D. (CM-SFE)
Grochol David, Ing., Ph.D.
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Publications

2019

  • MRÁZEK, V.; SEKANINA, L.; DOBAI, R.; SÝS, M.; ŠVENDA, P. Efficient On-Chip Randomness Testing Utilizing Machine Learning Techniques. IEEE Trans. on VLSI Systems., 2019, vol. 27, no. 12, p. 2734-2744. ISSN: 1063-8210. Detail

2018

  • GROCHOL, D.; SEKANINA, L. Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions. In European Conference on Genetic Programming. Lecture Notes in Computer Science. Berlin: Springer International Publishing, 2018. p. 187-202. ISBN: 978-3-319-77553-1. Detail
  • MRÁZEK, V.; SÝS, M.; VAŠÍČEK, Z.; SEKANINA, L.; MATYÁŠ, V. 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. p. 1302-1309. ISBN: 978-1-4503-5618-3. Detail

2017

  • GROCHOL, D.; SEKANINA, L. 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. p. 1533-1540. ISBN: 978-1-5090-4600-3. Detail
  • HUSA, J.; DOBAI, R. 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. p. 1825-1832. ISBN: 978-1-4503-4939-0. Detail
  • KIDOŇ, M.; DOBAI, R. 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. p. 1720-1727. ISBN: 978-1-5090-4601-0. Detail

2016

  • DOBAI, R.; KOŘENEK, J.; SEKANINA, L. Adaptive Development of Hash Functions in FPGA-Based Network Routers. In 2016 IEEE Symposium Series on Computational Intelligence. Athens: IEEE Computational Intelligence Society, 2016. p. 1-8. ISBN: 978-1-5090-4240-1. Detail
Back to top