Learning-based Approach to Analysis of Block Ciphers and Side-channel Attack

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === This paper aims to two problems – side-channel attack and identification of block ciphers. For the first problem a novel unsupervised learning approach is proposed for the task of Power Analysis – a form of side channel attack in Cryptanalysis. Different from s...

Full description

Bibliographic Details
Main Authors: Jung-Wei Chou, 周融瑋
Other Authors: Shou-De Lin
Format: Others
Language:en_US
Published: 2012
Online Access:http://ndltd.ncl.edu.tw/handle/51577101849503476477
id ndltd-TW-100NTU05392112
record_format oai_dc
spelling ndltd-TW-100NTU053921122015-10-13T21:50:19Z http://ndltd.ncl.edu.tw/handle/51577101849503476477 Learning-based Approach to Analysis of Block Ciphers and Side-channel Attack 以學習為本的方法於分組密碼分析及副通道攻擊 Jung-Wei Chou 周融瑋 碩士 國立臺灣大學 資訊工程學研究所 100 This paper aims to two problems – side-channel attack and identification of block ciphers. For the first problem a novel unsupervised learning approach is proposed for the task of Power Analysis – a form of side channel attack in Cryptanalysis. Different from some existing works that exploit supervised learning framework to this problem, our method does not require the labeled pairs which contains {X,Y}={key, power-trace} information for training, though is still capable of deciphering the secret key with high accuracy. A regression-based, unsupervised approach is proposed for this purpose. Later we further propose an enhanced model through exploiting the dependency of key bits between different rounds. Our experiment shows that the proposed method can outperform the state-of-the-art non-learning based decipherment methods. For the second problem we focus on cryptographic distinguishing attacks, in which the attacker is able to extract enough “information” from an encrypted message to distinguish it from a piece of random data, allow for powerful cryptanalysis both in theory and in practice. In this chapter, we report our experience of applying state-of-the-art machine learning techniques to launch cryptographic distinguishing attacks on several public datasets. We try several kinds of existing and new features on these datasets and found the ciphers’ “modes of operation” dominate the performance of classification tasks. When CBC mode is used with random initial vectors for each plaintext, the performance is extremely bad, while the performance for certain datasets is relatively good when ECB mode is used. We conclude that, in contrary to the findings of several existing works, the state-of-the-art machine learning techniques and cannot extract useful information from ciphertexts produced by modern ciphers operating in a reasonably secure mode such as CBC, let alone distinguish them from random data. Shou-De Lin 林守德 2012 學位論文 ; thesis 55 en_US
collection NDLTD
language en_US
format Others
sources NDLTD
description 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === This paper aims to two problems – side-channel attack and identification of block ciphers. For the first problem a novel unsupervised learning approach is proposed for the task of Power Analysis – a form of side channel attack in Cryptanalysis. Different from some existing works that exploit supervised learning framework to this problem, our method does not require the labeled pairs which contains {X,Y}={key, power-trace} information for training, though is still capable of deciphering the secret key with high accuracy. A regression-based, unsupervised approach is proposed for this purpose. Later we further propose an enhanced model through exploiting the dependency of key bits between different rounds. Our experiment shows that the proposed method can outperform the state-of-the-art non-learning based decipherment methods. For the second problem we focus on cryptographic distinguishing attacks, in which the attacker is able to extract enough “information” from an encrypted message to distinguish it from a piece of random data, allow for powerful cryptanalysis both in theory and in practice. In this chapter, we report our experience of applying state-of-the-art machine learning techniques to launch cryptographic distinguishing attacks on several public datasets. We try several kinds of existing and new features on these datasets and found the ciphers’ “modes of operation” dominate the performance of classification tasks. When CBC mode is used with random initial vectors for each plaintext, the performance is extremely bad, while the performance for certain datasets is relatively good when ECB mode is used. We conclude that, in contrary to the findings of several existing works, the state-of-the-art machine learning techniques and cannot extract useful information from ciphertexts produced by modern ciphers operating in a reasonably secure mode such as CBC, let alone distinguish them from random data.
author2 Shou-De Lin
author_facet Shou-De Lin
Jung-Wei Chou
周融瑋
author Jung-Wei Chou
周融瑋
spellingShingle Jung-Wei Chou
周融瑋
Learning-based Approach to Analysis of Block Ciphers and Side-channel Attack
author_sort Jung-Wei Chou
title Learning-based Approach to Analysis of Block Ciphers and Side-channel Attack
title_short Learning-based Approach to Analysis of Block Ciphers and Side-channel Attack
title_full Learning-based Approach to Analysis of Block Ciphers and Side-channel Attack
title_fullStr Learning-based Approach to Analysis of Block Ciphers and Side-channel Attack
title_full_unstemmed Learning-based Approach to Analysis of Block Ciphers and Side-channel Attack
title_sort learning-based approach to analysis of block ciphers and side-channel attack
publishDate 2012
url http://ndltd.ncl.edu.tw/handle/51577101849503476477
work_keys_str_mv AT jungweichou learningbasedapproachtoanalysisofblockciphersandsidechannelattack
AT zhōuróngwěi learningbasedapproachtoanalysisofblockciphersandsidechannelattack
AT jungweichou yǐxuéxíwèiběndefāngfǎyúfēnzǔmìmǎfēnxījífùtōngdàogōngjī
AT zhōuróngwěi yǐxuéxíwèiběndefāngfǎyúfēnzǔmìmǎfēnxījífùtōngdàogōngjī
_version_ 1718069391221850112