TY - GEN
T1 - S-NET
T2 - 20th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2020
AU - Aljuffri, Abdullah
AU - Venkatachalam, Pradeep
AU - Reinbrecht, Cezar
AU - Hamdioui, Said
AU - Taouil, Mottaqiallah
PY - 2020
Y1 - 2020
N2 - Side channel attacks are recognized as one of the most powerful attacks due to their ability to extract secret key information by analyzing the unintended leakage generated during operation. This makes them highly attractive for attackers. The current countermeasures focus on either randomizing the leakage by obfuscating the power consumption of all operations or blinding the leakage by maintaining a similar power consumption for all operations. Although these techniques help hiding the power-leakage correlation, they do not remove the correlation completely. This paper proposes a new countermeasure type, referred to as confusion, that aims to break the linear correlation between the leakage model and the power consumption and hence confuses attackers. It realizes this by replacing the traditional SBOX implementation with a neural network referred to as S-NET. As a case study, the security of Advanced Encryption Standard (AES) software implementations with both conventional SBOX and S-NET are evaluated. Based on our experimental results, S-NET leaks no information and is resilient against popular attacks such as differential and correlation power analysis.
AB - Side channel attacks are recognized as one of the most powerful attacks due to their ability to extract secret key information by analyzing the unintended leakage generated during operation. This makes them highly attractive for attackers. The current countermeasures focus on either randomizing the leakage by obfuscating the power consumption of all operations or blinding the leakage by maintaining a similar power consumption for all operations. Although these techniques help hiding the power-leakage correlation, they do not remove the correlation completely. This paper proposes a new countermeasure type, referred to as confusion, that aims to break the linear correlation between the leakage model and the power consumption and hence confuses attackers. It realizes this by replacing the traditional SBOX implementation with a neural network referred to as S-NET. As a case study, the security of Advanced Encryption Standard (AES) software implementations with both conventional SBOX and S-NET are evaluated. Based on our experimental results, S-NET leaks no information and is resilient against popular attacks such as differential and correlation power analysis.
KW - Advanced Encryption Standard
KW - Neural network
KW - S-NET
KW - SBOX
KW - Side channel analysis
UR - http://www.scopus.com/inward/record.url?scp=85093872667&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60939-9_20
DO - 10.1007/978-3-030-60939-9_20
M3 - Conference contribution
AN - SCOPUS:85093872667
SN - 9783030609382
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 295
EP - 307
BT - Embedded Computer Systems
A2 - Orailoglu, Alex
A2 - Jung, Matthias
A2 - Reichenbach, Marc
PB - Springer
Y2 - 5 July 2020 through 9 July 2020
ER -