TY - GEN
T1 - Secret key classification based on electromagnetic analysis and feature extraction using machine-learning approach
AU - Mukhtar, Naila
AU - Kong, Yinan
PY - 2018
Y1 - 2018
N2 - Despite having a secure algorithm running on a cryptographic chip, in an embedded system device on the network, secret private data is still vulnerable due to Side-Channel leakage information. In this paper, we have focused on retrieving secret-key information obtained from one of the Side Channels, namely Electromagnetic radiation signals. We have captured leaked Electromagnetic signals from a Kintex-7 FPGA, while AES is running over it, and analyzed them using machine and deep-learning based algorithms to classify each bit of the key. Moreover, we aim to analyze the effect of having different signal properties as features in these classification algorithms. The results will help in defining which features give maximum information about the captured signal, hence leading to key recovery.
AB - Despite having a secure algorithm running on a cryptographic chip, in an embedded system device on the network, secret private data is still vulnerable due to Side-Channel leakage information. In this paper, we have focused on retrieving secret-key information obtained from one of the Side Channels, namely Electromagnetic radiation signals. We have captured leaked Electromagnetic signals from a Kintex-7 FPGA, while AES is running over it, and analyzed them using machine and deep-learning based algorithms to classify each bit of the key. Moreover, we aim to analyze the effect of having different signal properties as features in these classification algorithms. The results will help in defining which features give maximum information about the captured signal, hence leading to key recovery.
KW - Side-Channel analysis
KW - Embedded system security
KW - Signal-processing
KW - Machine-learning classification
KW - Neural-network classification
UR - http://www.scopus.com/inward/record.url?scp=85049639403&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-94421-0_6
DO - 10.1007/978-3-319-94421-0_6
M3 - Conference proceeding contribution
AN - SCOPUS:85049639403
SN - 9783319944203
T3 - Communications in Computer and Information Science
SP - 80
EP - 92
BT - Future network systems and security
A2 - Doss, Robin
A2 - Piramuthu, Selwyn
A2 - Zhou, Wei
PB - Springer, Springer Nature
CY - Cham
T2 - 4th International Conference on Future Network Systems and Security, FNSS 2018
Y2 - 9 July 2018 through 11 July 2018
ER -