TY - GEN
T1 - One trace is all it takes
T2 - 9th International Conference on Security, Privacy, and Applied Cryptography Engineering, SPACE 2019
AU - Weissbart, Léo
AU - Picek, Stjepan
AU - Batina, Lejla
PY - 2019
Y1 - 2019
N2 - Profiling attacks, especially those based on machine learning proved as very successful techniques in recent years when considering side-channel analysis of block ciphers implementations. At the same time, the results for implementations of public-key cryptosystems are very sparse. In this paper, we consider several machine learning techniques in order to mount a power analysis attack on EdDSA using the curve Curve25519 as implemented in WolfSSL. The results show all considered techniques to be viable and powerful options. Especially convolutional neural networks (CNNs) are effective as we can break the implementation with only a single measurement in the attack phase while requiring less than 500 measurements in the training phase. Interestingly, that same convolutional neural network was recently shown to perform extremely well for attacking the implementation of the AES cipher. Our results show that some common grounds can be established when using deep learning for profiling attacks on distinct cryptographic algorithms and their corresponding implementations.
AB - Profiling attacks, especially those based on machine learning proved as very successful techniques in recent years when considering side-channel analysis of block ciphers implementations. At the same time, the results for implementations of public-key cryptosystems are very sparse. In this paper, we consider several machine learning techniques in order to mount a power analysis attack on EdDSA using the curve Curve25519 as implemented in WolfSSL. The results show all considered techniques to be viable and powerful options. Especially convolutional neural networks (CNNs) are effective as we can break the implementation with only a single measurement in the attack phase while requiring less than 500 measurements in the training phase. Interestingly, that same convolutional neural network was recently shown to perform extremely well for attacking the implementation of the AES cipher. Our results show that some common grounds can be established when using deep learning for profiling attacks on distinct cryptographic algorithms and their corresponding implementations.
UR - http://www.scopus.com/inward/record.url?scp=85076509801&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35869-3_8
DO - 10.1007/978-3-030-35869-3_8
M3 - Conference contribution
AN - SCOPUS:85076509801
SN - 9783030358686
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 86
EP - 105
BT - Security, Privacy, and Applied Cryptography Engineering - 9th International Conference, SPACE 2019, Proceedings
A2 - Bhasin, Shivam
A2 - Mendelson, Avi
A2 - Nandi, Mridul
PB - Springer
Y2 - 3 December 2019 through 7 December 2019
ER -