Combinatorial techniques for universal hashing
Journal of Computer and System Sciences
Security in computing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Contemporary Cryptology: The Science of Information Integrity
Contemporary Cryptology: The Science of Information Integrity
Handbook of Applied Cryptography
Handbook of Applied Cryptography
A Design Principle for Hash Functions
CRYPTO '89 Proceedings of the 9th Annual International Cryptology Conference on Advances in Cryptology
Finding collisions in the full SHA-1
CRYPTO'05 Proceedings of the 25th annual international conference on Advances in Cryptology
How to break MD5 and other hash functions
EUROCRYPT'05 Proceedings of the 24th annual international conference on Theory and Applications of Cryptographic Techniques
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The strength of message authentication, digital signature and pseudonym generation mechanisms relies on the quality of the one-way hash functions used. In this paper, we propose two tests based on computational intelligence and evolutionary algorithms theory to assess the hash function quality, which may be used along with other known methods and thus comprise a testing methodology. Based on the known nonlinearity test, which might confirm uniformity of digests, we formulate two tests using Support Vector Machines (SVM)/ MLP neural networks as well as Genetic Algorithms (GA). Both tests attempt to confirm that the produced digests cannot be modeled and, moreover, that it is impossible to find two or more messages that lead to a given digest apart from involving brute force computations. Both tests are applied to confirm the quality of the well-known MD5 and SHA message digest algorithms.