Identifying Internet of Things software activities using deep learning-based electromagnetic side-channel analysis

Internet of Things (IoT) is becoming the new frontier in digital forensics due to the abundance of IoT devices appearing in day-to-day life. The diversity and complexity of IoT ecosystems pose a considerable challenge to digital investigators that demand novel approaches. Electromagnetic side-channel analysis (EM-SCA) has been proposed as a promising window to gather forensically useful information from IoT devices. Machine Learning (ML) techniques are instrumental when performing EM-SCA on IoT devices. Our work aims to investigate how machine learning can be applied to accurately identify complex activities on IoT devices from their generated electromagnetic noises. To this end, a range of classification models were created, including deep learning models, to predict the activity from the electromagnetic noise emitted while the device performed the activities. A dataset was generated by using ten different well-known sorting algorithms with diverse computational time complexities and running them on an Arduino Leonardo device to represent a low-powered IoT device. The algorithms were continually sorting arrays of 100 elements randomly generated in ascending order. Experiments were conducted to identify which ML methods performed better with the generated data sets. Furthermore, more experiments were conducted to identify how the methods perform depending on the window size of raw samples and the number of examples against which they are trained. From the experimental results, it is possible to predict which activity is being executed with a high level of accuracy (99.6%) with a convolutional neural network (CNN). It was also found that Random Forests (RF) and Deep Learning (DL) are suitable ML models for making predictions with EM-SCA.