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

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 different key sizes 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 extracted model from the test data in combination with combinatorial coverage measurements allows to extract information about the structure of the test vectors. Our analysis shows that some test sets 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.
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Submitted on : Thursday, March 26, 2020 - 1:47:32 PM
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Dimitris Simos, Bernhard Garn, Ludwig Kampel, D. Kuhn, Raghu Kacker. Knowledge Extraction for Cryptographic Algorithm Validation Test Vectors by Means of Combinatorial Coverage Measurement. 3rd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2019, Canterbury, United Kingdom. pp.195-208, ⟨10.1007/978-3-030-29726-8_13⟩. ⟨hal-02520031⟩

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