Published: August 26, 2019
Author(s)
Dimitris Simos (SBA Research), Bernhard Garn (SBA Research), Ludwig Kampel (SBA Research), Richard Kuhn (NIST), Raghu Kacker (NIST)
Conference
Name: International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
Dates: 08/26/2019 - 08/29/2019
Location: Canterbury, UK
Citation: CD-MAKE 2019: Machine Learning and Knowledge Extraction, vol. 11713, pp. 195-208
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.
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...
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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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Keywords
combinatorial measurement; cryptographic applications; data analysis; knowledge extraction
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