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Knowledge Extraction for Cryptographic Algorithm Validation Test Vectors by Means of Combinatorial Coverage Measurement

Published

Author(s)

Dimitris Simos, Bernhard Garn, Ludwig Kampel, David R. Kuhn, Raghu N. Kacker

Abstract

We present a combinatorial coverage measurement analysis for test vectors provided by the NIST Cryptographic Algorithm Validation Program (CAVP), and in particular for test vectors targeting the AES block ciphers for di erent key lengths and cryptographic modes of operation. These test vectors are measured and analyzed using a combinatorial approach, which was made feasible via developing the necessary input models. The combinatorial model for the test data in combination with the coverage measurement allows to extract information about the structure of the test vectors. Our analysis shows that some test vectors do not achieve full combinatorial coverage. It is further discussed how this retrieved knowledge could be used as a means of test quality analysis, by incorporating residual risk estimation techniques based on combinatorial methods, in order to assist the overall validation testing procedure.
Volume
11713
Conference Dates
August 26-29, 2019
Conference Location
Canterbury, -1
Conference Title
IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction

Keywords

Combinatorial Measurement, Cryptographic Applications, Data Analysis, Knowledge Extraction
Created August 23, 2019, Updated March 10, 2020