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 a Martin UKROP

Základní údaje

Originální název

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

Autoři

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

Vydání

Infocommunications Journal, Budapest, Scientific Association for Infocommunications, 2016, 2061-2079

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Oficiální stránky časopisu s dostupným plným textem

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/16:00088384

Organizační jednotka

Fakulta informatiky

UT WoS

000382864400002

EID Scopus

2-s2.0-84964845779

Klíčová slova anglicky

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

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 17. 4. 2019 11:11, RNDr. Martin Ukrop, Ph.D.

Anotace

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.

Návaznosti

GA16-08565S, projekt VaV

Název: Rozvoj kryptoanalytických metod prostřednictvím evolučních výpočtů

Investor: Grantová agentura ČR, Advancing cryptanalytic methods through evolutionary computing

Citovat

KUBÍČEK, Karel; Jiří NOVOTNÝ; Petr ŠVENDA a Martin UKROP. New results on reduced-round Tiny Encryption Algorithm using genetic programming. Infocommunications Journal. Budapest: Scientific Association for Infocommunications, 2016, roč. 8, č. 1, s. 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 a Martin UKROP. New results on reduced-round Tiny Encryption Algorithm using genetic programming. \textit{Infocommunications Journal}. Budapest: Scientific Association for Infocommunications, 2016, roč.~8, č.~1, s.~2-9. ISSN~2061-2079.

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