Can Machine Learn Pipeline Leakage?

Side-channel attacks cause a significant threat to security implementations in embedded devices. Accordingly, an automated framework simulating side-channel behaviours can offer invaluable insights into leakage origins and characteristics, helping developers improve those devices during the design phase. While there has been a substantial effort towards crafting leakage simulators, earlier methods either necessitated significant manual work for reverse engineering the micro-architectural layer or depended on Deep Learning (DL) models where the neural network's complexity increased considerably with the addition of pipeline stages. This paper presents a novel modelling approach using Recurrent Neural Networks (RNNs) to construct instruction-level power models that exhibit enhanced performance in detecting pipeline leakage. Our findings indicate that with memory-based machine learning models, it becomes unnecessary to input data accounting for the pipeline effect. This strategy reduces feature dimensionality by at least one-third for a three-stage pipeline, albeit at a modest compromise in model performance. This reduced feature set underscores our model's scalability, making it a preferred choice for analyzing microprocessors with extended pipeline stages. Importantly, our methodology accelerates the micro-architectural profiling phase in side-channel simulator design. When evaluated on an expansive dataset, the performance of our memory-based model closely matches that of the Multilayer Perceptron (MLP) with an R2 value of 0.79. On a reduced dataset (removing the pipeline effect), our model achieves an R2 value of 0.65, outperforming the MLP, which reaches an R2 value of 0.39. Moreover, our model is designed with scalability in mind, making it suitable for profiling microcontrollers with advanced pipeline stages. For the practical realisation of our approach, we employed the open-source ABBY-CM0 dataset from the ARM Cortex-M0 microcontroller, which has three pipeline stages. To provide a detailed analysis, we also consider a Convolutional Neural Network (CNN) besides two RNN architectures (Long Short-Term Memory and Gated Recurrent Unit).