New results on reduced-round Tiny Encryption Algorithm using genetic programming
| Authors | |
|---|---|
| Year of publication | 2016 |
| Type | Article in Periodical |
| Magazine / Source | Infocommunications Journal |
| MU Faculty or unit | |
| Citation | |
| Web | Oficiální stránky časopisu s dostupným plným textem |
| Field | Informatics |
| Keywords | randomness statistical testing; TEA; genetic algorithms; randomness distinguisher; software circuit |
| Description | Analysis of cryptoprimitives usually requires extensive work of a skilled cryptanalyst. Some automation is possible, e.g. by using randomness testing batteries such as Statistical Test Suite from NIST (NIST STS) or Dieharder. Such batteries compare the statistical properties of the functions output stream to the theoretical values. A potential drawback is a limitation to predefined tested patterns. However, there is a new approach EACirc is a genetically inspired randomness testing framework based on finding a dynamically constructed test. This test works as a probabilistic distinguisher separating cipher outputs from truly random data. In this work, we use EACirc to analyze the outputs of Tiny Encryption Algorithm (TEA). TEA was selected as a frequently used benchmark algorithm for cryptanalytic approaches based on genetic algorithms. In this paper, we provide results of EACirc applied to TEA ciphertext created from differently structured plaintext. We compare the methodology and results with previous approaches for limited-round TEA. A different construction of EACirc tests also allows us to determine which part of ciphers output is relevant to the decision of a well-performing randomness distinguisher. |
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