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

J 2016

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

KUBÍČEK, Karel; Jiří NOVOTNÝ; Petr ŠVENDA and Martin UKROP

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

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

References:

Oficiální stránky časopisu s dostupným plným textem

RIV identification code

RIV/00216224:14330/16:00088384

Organization unit

Faculty of Informatics

UT WoS

000382864400002

EID Scopus

2-s2.0-84964845779

Keywords in English

randomness statistical testing; TEA; genetic algorithms; randomness distinguisher; software circuit

Abstract

V originále

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

Cite

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

@article{1362660,
   author = {Kubíček, Karel and Novotný, Jiří and Švenda, Petr and Ukrop, Martin},
   article_location = {Budapest},
   article_number = {1},
   keywords = {randomness statistical testing; TEA; genetic algorithms; randomness distinguisher; software circuit},
   language = {eng},
   issn = {2061-2079},
   journal = {Infocommunications Journal},
   title = {New results on reduced-round Tiny Encryption Algorithm using genetic programming},
   url = {http://www.infocommunications.hu/2016_1},
   volume = {8},
   year = {2016}
}

TY  - JOUR
ID  - 1362660
AU  - Kubíček, Karel - Novotný, Jiří - Švenda, Petr - Ukrop, Martin
PY  - 2016
TI  - New results on reduced-round Tiny Encryption Algorithm using genetic programming
JF  - Infocommunications Journal
VL  - 8
IS  - 1
SP  - 2-9
EP  - 2-9
PB  - Scientific Association for Infocommunications
SN  - 20612079
KW  - randomness statistical testing
KW  - TEA
KW  - genetic algorithms
KW  - randomness distinguisher
KW  - software circuit
UR  - http://www.infocommunications.hu/2016_1
L2  - http://www.infocommunications.hu/2016_1
N2  - 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.
ER  -

KUBÍČEK, Karel; Jiří NOVOTNÝ; Petr ŠVENDA and Martin UKROP. New results on reduced-round Tiny Encryption Algorithm using genetic programming. \textit{Infocommunications Journal}. Budapest: Scientific Association for Infocommunications, 2016, vol.~8, No~1, p.~2-9. ISSN~2061-2079.

Přehled o publikaci