TY - GEN
T1 - On the effectiveness of using state-of-the-art machine learning techniques to launch cryptographic distinguishing attacks
AU - Chou, Jung Wei
AU - Lin, Shou De
AU - Cheng, Chen Mou
PY - 2012
Y1 - 2012
N2 - 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 paper, 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 find that the ciphers' "modes of operation" dominate the performance of classification tasks. When CBC mode is used with a random initial vector 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 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.
AB - 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 paper, 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 find that the ciphers' "modes of operation" dominate the performance of classification tasks. When CBC mode is used with a random initial vector 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 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.
KW - Computer Forensics
KW - Cryptographic Distinguishing Attacks
KW - Identification of Encryption Algorithm
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=84869747258&partnerID=8YFLogxK
U2 - 10.1145/2381896.2381912
DO - 10.1145/2381896.2381912
M3 - 会议稿件
AN - SCOPUS:84869747258
SN - 9781450316644
T3 - Proceedings of the ACM Conference on Computer and Communications Security
SP - 105
EP - 109
BT - AISec'12 - Proceedings of the ACM Workshop on Security and Artificial Intelligence
T2 - 5th ACM Workshop on Artificial Intelligence and Security, AISec 2012
Y2 - 19 October 2012 through 19 October 2012
ER -