Deep learning-based Electromagnetic Side-Channel Analysis for the Investigation of IoT Devices

The boom of Internet of Things (IoT) devices has brought along new security concerns which earlier were not thought of. This has expanded the potential for digital forensic investigators to gather rich evidence from these sources. The data on these IoT devices is not easily accessible due to the lack of proper techniques to investigate such devices. This paper presents a sophisticated non-invasive method to investigate IoT devices. The software activities running on a device can be inspected by observing the electromagnetic radiation emitted from the devices during the process. The objective of this project is to evaluate if it is possible to classify the software activities being run on an IoT device by performing an electromagnetic side-channel analysis (EM-SCA). This paper presents a methodology for analyzing the EM side-channels and classifying encryption algorithms being run on a Raspberry Pi 4. This work demonstrates that the cryptographic encryptions can be classified with over 95% accuracy by using deep neural network-based classifiers.