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Revealing IoT Cryptographic Settings through Electromagnetic Side-Channel Analysis
Congratulations to Muhammad Rusyaidi Zunaidi and co-authors Asanka Sayakkara, and Mark Scanlon on the publication of Revealing IoT Cryptographic Settings through Electromagnetic Side-Channel Analysis in Electronics.
Co-authors: Asanka Sayakkara, and Mark Scanlon.
AI-generated summary of the contribution: This research delves into the capabilities of Electromagnetic Side-Channel Analysis (EM-SCA) for detecting cryptographic key lengths and algorithms employed in IoT devices. The study utilizes a machine learning-based approach, specifically Support Vector Machine (SVM) and Logistic Regression, to analyze EM signals emitted by IoT devices during cryptographic operations. The results demonstrate a notable accuracy of 94.55% in distinguishing between Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) operations. This method has significant implications for digital forensic investigations, offering a novel approach for uncovering encrypted data’s cryptographic settings. The study’s findings contribute to enhancing digital forensic capabilities in encrypted data investigation, providing a methodological advancement for non-invasively uncovering cryptographic settings in IoT devices.