## 6 Conclusions  
This study proposed a novel framework for generating and evaluating dummy power traces in a fully virtualized and controlled environment, enabling reproducible and cost-effective experimentation in SCA. By systematically training DL models on both original and generated dummy traces, the results demonstrated that executions protected by dummy insertion still exhibit exploitable leakage patterns. Empirical evaluations using RNN, Bi-RNN, and MLP models confirmed their effectiveness in distinguishing dummy traces from genuine ones, thereby enabling the models to circumvent traditional hiding-based countermeasures by selectively identifying residual leakage from actual operations.  
These findings reaffirm that static obfuscation techniques, while increasing the complexity of side-channel traces, are insufficient to provide robustness against DL-based attacks. By incorporating complex microarchitectural features and a diverse set of instruction types into the trace generation process, the proposed framework enhances the realism and cross-platform applicability of DL-SCA. This increased generalizability enables DL models to effectively adapt to a wide range of heterogeneous devices. As a result, any CPS device accessible to an adversary may be vulnerable to such attacks. These observations underscore the need for adaptive, dynamic, and learning-driven defense mechanisms that can respond to evolving attack strategies.  
An essential next step is to develop integrated evaluation frameworks that not only assess information recovery capabilities but also incorporate mechanisms for distinguishing between genuine and obfuscated operations. Embedding such discriminative capacity into DL-based assessment pipelines would provide a more interpretable and security-relevantcharacterization of vulnerabilities in embedded systems. Furthermore, to improve its practical applicability, it is essential to evaluate the framework under real-world noise and hardware conditions [38].  
In parallel, future research should extend the proposed framework by incorporating adversarial ML techniques to enhance DL-SCA capabilities, particularly in the context of data poisoning and model robustness [24; 26]. On the defensive side, static obfuscation techniques should be complemented or replaced by dynamic obfuscation strategies that adapt leakage patterns over time, thereby enabling more resilient protection for IoT and CPS environments [12; 22; 28].  
Overall, this study contributes a reproducible and extensible methodology for DL-SCA, while highlighting the urgent need for countermeasures that are not only technically robust but also adaptable to the rapidly evolving landscape of AI-driven attacks.  
Conceptualization, S.P., A.S. and Y.S.; methodology, S.P., A.S., M.C. and Y.S.; software, A.S., M.C. and H.K.; validation, S.P. and A.S.; formal analysis, A.S., M.C., J.K. and Y.S.; investigation, S.P., A.S. and M.C.; resources, S.P. and A.S.; data curation, A.S. and M.C.; writing--original draft preparation, S.P., A.S. and M.C.; writing--review and editing, S.P. and Y.S.; visualization, A.S. and M.C.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the commercialization promotion agency for R&D outcomes grant funded by the Korea government (MSIT) (RS-2025-02315174). This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 2022400000020).  
Not applicable.  
The SCADataset generated in this study can be accessed at [[https://github.com/PLASS-Lab/SCADataset](https://github.com/PLASS-Lab/SCADataset)]([https://github.com/PLASS-Lab/SCADataset](https://github.com/PLASS-Lab/SCADataset)) (accessed on 5 June 2025) [37].  
The authors declare no conflicts of interest.