The paper "Ablation Analysis for Multi-device Deep Learning-based Physical Side-channel Analysis" by Wu et al. addresses the challenge of portability in side-channel analysis (SCA) when using deep learning models trained on a single device to attack multiple devices. The authors propose a new methodology based on ablation analysis to assess the sensitivity and resilience of each layer in a neural network, aiming to create a Multiple Device Model from Single Device (MDMSD). This approach involves partially ablating specific layers and performing recovery training to evaluate the impact of these ablations on the model's performance. The key contributions of the paper include: 1. **Ablation Analysis**: A new methodology for assessing the importance of each layer in a neural network, providing insights into how the network functions. 2. **Layer Assessment Criteria**: Introduce two criteria—sensitivity and resilience—to evaluate the impact of layer ablations. 3. **MDMSD Approach**: Use the insights from layer assessment to create a model that can generalize from a single device to multiple devices, addressing the portability problem. 4. **Experimental Results**: Demonstrate the effectiveness of the MDMSD approach through extensive experiments on various datasets, showing improved performance compared to existing methods. The paper highlights the practical challenges of SCA, particularly the variability in leakage traces across different devices, and proposes a solution that enhances the portability and effectiveness of deep learning-based SCA.The paper "Ablation Analysis for Multi-device Deep Learning-based Physical Side-channel Analysis" by Wu et al. addresses the challenge of portability in side-channel analysis (SCA) when using deep learning models trained on a single device to attack multiple devices. The authors propose a new methodology based on ablation analysis to assess the sensitivity and resilience of each layer in a neural network, aiming to create a Multiple Device Model from Single Device (MDMSD). This approach involves partially ablating specific layers and performing recovery training to evaluate the impact of these ablations on the model's performance. The key contributions of the paper include: 1. **Ablation Analysis**: A new methodology for assessing the importance of each layer in a neural network, providing insights into how the network functions. 2. **Layer Assessment Criteria**: Introduce two criteria—sensitivity and resilience—to evaluate the impact of layer ablations. 3. **MDMSD Approach**: Use the insights from layer assessment to create a model that can generalize from a single device to multiple devices, addressing the portability problem. 4. **Experimental Results**: Demonstrate the effectiveness of the MDMSD approach through extensive experiments on various datasets, showing improved performance compared to existing methods. The paper highlights the practical challenges of SCA, particularly the variability in leakage traces across different devices, and proposes a solution that enhances the portability and effectiveness of deep learning-based SCA.