New results on reduced-round Tiny Encryption Algorithm using genetic programming

KUBÍČEK, Karel, Jiří NOVOTNÝ, Petr ŠVENDA and Martin UKROP. New results on reduced-round Tiny Encryption Algorithm using genetic programming. Online. Infocommunications Journal. Budapest: Scientific Association for Infocommunications, 2016, vol. 8, No 1, p. 2-9. ISSN 2061-2079. [citováno 2024-04-23]

Basic information

• Original name: New results on reduced-round Tiny Encryption Algorithm using genetic programming
• Authors: KUBÍČEK, Karel (203 Czech Republic, guarantor, belonging to the institution), Jiří NOVOTNÝ (203 Czech Republic, belonging to the institution), Petr ŠVENDA (203 Czech Republic, belonging to the institution) and Martin UKROP (703 Slovakia, belonging to the institution)
• Edition: Infocommunications Journal, Budapest, Scientific Association for Infocommunications, 2016, 2061-2079.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United Kingdom of Great Britain and Northern Ireland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: Oficiální stránky časopisu s dostupným plným textem
• RIV identification code: RIV/00216224:14330/16:00088384
• Organization unit: Faculty of Informatics
• UT WoS: 000382864400002
• Keywords in English: randomness statistical testing; TEA; genetic algorithms; randomness distinguisher; software circuit
• Tags: International impact, Reviewed

Abstract

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.

Links

• GA16-08565S, research and development project: Name: Rozvoj kryptoanalytických metod prostřednictvím evolučních výpočtů

Investor: Czech Science Foundation