Neural network based benchmarks in the quality assessment of message digest algorithms for digital signatures based secure Internet communications

The strength of data integrity, message authentication and pseudonym generation mechanisms in the design of secure multimedia communication applications over the Internet relies on the quality of the message digest algorithms used in the digital signatures construction/verification process. In this paper, we propose neural network based evaluation benchmarks to assess the message digest function quality since there is lack of practical tests to be applied to message digest algorithms in the emerging field of designing secure information and communication systems especially for the delivery of multimedia content, where the issues of copyright protection and security in transactions are outstanding. These assessment tests are suggested here along with other ones derived from well known statistical and information theoretic methods, such as entropy test, and thus comprise a suitable practical evaluation methodology.