TY - GEN
T1 - From Code to EM Signals
T2 - 19th International Conference on Availability, Reliability and Security, ARES 2024
AU - Vedros, Kurt A.
AU - Kolias, Constantinos
AU - Barbara, Daniel
AU - Ivans, Robert C.
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2024/7/30
Y1 - 2024/7/30
N2 - Today, it is possible to perform external anomaly detection by analyzing the involuntary EM emanations of digital device components. However, one of the most important challenges of these methods is the manual collection of EM signals for fingerprinting. Indeed, this procedure must be conducted by a human expert and requires high precision. In this work, we introduce a framework that alleviates this requirement by relying on synthetic EM signals that have been generated from assembly code. The signals are produced with the use of a Generative Adversarial Network (GAN) model. Experimentally, we identify that the synthetic EM signals are extremely similar to the real and thus, can be used for training anomaly detection models effectively. Through experimental assessments, we prove that the anomaly detection models are capable of recognizing even minute alterations to the code with high accuracy.
AB - Today, it is possible to perform external anomaly detection by analyzing the involuntary EM emanations of digital device components. However, one of the most important challenges of these methods is the manual collection of EM signals for fingerprinting. Indeed, this procedure must be conducted by a human expert and requires high precision. In this work, we introduce a framework that alleviates this requirement by relying on synthetic EM signals that have been generated from assembly code. The signals are produced with the use of a Generative Adversarial Network (GAN) model. Experimentally, we identify that the synthetic EM signals are extremely similar to the real and thus, can be used for training anomaly detection models effectively. Through experimental assessments, we prove that the anomaly detection models are capable of recognizing even minute alterations to the code with high accuracy.
KW - Anomaly Detection
KW - Generative Artificial Networks
KW - Side Channel Analysis
UR - https://www.scopus.com/pages/publications/85200327655
UR - https://www.mendeley.com/catalogue/fe222058-c0a0-3459-94ac-de68c2a744e9/
U2 - 10.1145/3664476.3664520
DO - 10.1145/3664476.3664520
M3 - Conference contribution
AN - SCOPUS:85200327655
T3 - Proceedings of the 19th International Conference on Availability, Reliability and Security
BT - ARES 2024 - 19th International Conference on Availability, Reliability and Security, Proceedings
PB - Association for Computing Machinery
Y2 - 30 July 2024 through 2 August 2024
ER -