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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F16%3A00088384" target="_blank" >RIV/00216224:14330/16:00088384 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.infocommunications.hu/2016_1" target="_blank" >http://www.infocommunications.hu/2016_1</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/GA16-08565S" target="_blank" >GA16-08565S: Rozvoj kryptoanalytických metod prostřednictvím evolučních výpočtů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Infocommunications Journal

ISSN

2061-2079

e-ISSN

—

Svazek periodika

8

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

2-9

Kód UT WoS článku

000382864400002

EID výsledku v databázi Scopus

—