## 5 Discussion  
This study systematically evaluated the effectiveness of dummy power traces in mitigating SCAs when analyzed using DL models. Using a simulated environment built on QEMU, GDB, and ELMO, a large-scale dataset of power traces was generated, systematically incorporating dummy traces to analyze their impact on cryptographic security. Experiments using RNN, Bi-RNN, and MLP models demonstrated that although dummy insertion increases trace complexity, DL-based classifiers consistently achieved high accuracy in distinguishing real traces from dummy ones. The average F1-score of 97.65% indicates that conventional hiding techniques are insufficient against the inference capabilities of DL-SCAs. To evaluate the ability of the attack models to bypass dummy-based obfuscation, a balanced dataset was deliberately constructed, consisting of an equal ratio of original and dummy traces. While this design facilitated the evaluation of DL models under controlled conditions, subsequent studies should investigate the effects of varying the proportion of dummy traces, both in the obfuscation scheme and the training dataset. Such an analysis could enable a more rigorous quantification of the relationship between trace complexity and security robustness. The simulation-based methodology used in this study offers a notable advantage, as it allows for the precise generation of intended data under fully controlled conditions [18]. However, it also introduces several limitations. First, ELMO supports only a limited subset of instructions from the target microcontroller, as detailed in Table 3 (Section 2.3), which necessitated grouping of instruction types and use of representative categories in the experiments. Second, the generated traces, although realistic in terms of leakage patterns, lack the data-independent noise typically observed in real-world environments. As a result, models trained on simulated traces may not fully generalize to physical hardware [29]. Addressing these limitations, such as by incorporating synthetic noise models or adopting hybrid simulation-real data training, could substantially enhance the realism and practical relevance of simulation-based side-channel research. Building on these experimental insights, the results demonstrated that DL models can distinguish genuine traces from dummy ones with high reliability. This capability reveals a critical vulnerability in hiding-based defenses. While conventional evaluations of DL-SCAs rely on metrics such as classification accuracy, F1-score, or guessing entropy [15; 16; 17; 18; 19; 20; 21], these performance indicators alone are insufficient to determine whether the recovered outputs correspond to meaningful operations or artifacts of obfuscation. Given that DL-SCAs can infer execution semantics and explicitly detect obfuscation patterns, evaluating their threat potential necessitates more nuanced and multi-dimensional assessment criteria. In particular, incorporating physical-layer indicators such as SNR into the evaluation framework can provide a complementary perspective on leakage severity and help quantify the conditions under which obfuscation fails. Future assessments should consider not only prediction performance but also the model's ability to recover functional behavior and to detect or circumvent obfuscation strategies under varying leakage conditions. To further enhance the offensive capabilities of DL-SCAs, future research should investigate the application of adversarial ML techniques. Specifically, emphasis should be placed on data filtering and robustness evaluation as essential countermeasures against model degradation caused by data poisoning attacks [24; 29]. Additionally, to improve the practical applicability of the research findings, it is necessary to evaluate the effects of measurement errors and environmental noise, factors not typically present in simulation environments, through real hardware implementations [39].