## 1 Introduction  
The increasing integration of embedded systems and Internet of Things (IoT) devices within cyber-physical systems (CPSs) has raised significant cybersecurity concerns. Among these threats, side-channel attacks (SCAs) pose a substantial challenge, as they exploit unintended information leakage, such as power consumption, electromagnetic emissions, and execution timing, to extract sensitive cryptographic information [1, 2]. CPS security research has traditionally focused on cyber-layer protections, such as network-level anomaly detection and intrusion detection systems (IDSs). In particular,artificial intelligence (AI) has been widely adopted in these domains, and there is a growing trend toward constructing highly refined datasets to improve detection performance [3,4]. As the field has matured, the focus has shifted from generic network defenses to threats specific to programmable logic controllers (PLCs), which constitute the computational core of most industrial plants. Recent comprehensive surveys have documented that PLC vulnerabilities span firmware, ladder logic, and fieldbus layers and remain a persistent root cause of CPS incidents [5,6]. Complementing these diagnostic efforts, control-theoretic studies have introduced adaptive secure-control schemes that maintain plant safety even when sensor or actuator channels are actively compromised, thereby closing some but not all of the gaps between cyber and physical security domains [7]. Nevertheless, these logic- and control-layer defenses still leave CPSs vulnerable to SCAs that exploit leakages from the underlying hardware. Defense mechanisms such as logic locking have been proposed to counter traditional SCAs [8]. However, with the proliferation of deep learning (DL), empirical evidence indicates that compact convolutional neural network (CNN) architectures can recover AES keys from a single power trace on resource-constrained microcontrollers [9]. Moreover, multi-leak DL pipelines remain effective even when leakage is fragmented across multiple temporal windows, highlighting the robustness of modern SCA methodologies [10]. Even PLCs that do not process cryptographic workloads remain vulnerable. Specifically, cache-based SCAs targeting software-defined PLCs have been shown to reveal control logic and compromise process integrity [11]. Figure 1 illustrates the workflow of a profiling-based SCA using DL. First, the dataset is constructed using a template device, on which a model is trained. Then, power traces are collected from the target device, and the trained model is exploited to extract sensitive information. While various countermeasures, such as hiding and masking techniques, have been developed to mitigate these threats, recent advancements in DL have revealed their inherent vulnerabilities [12]. Traditional hiding techniques aim to obfuscate power consumption patterns, making it difficult for attackers to extract meaningful information. However, machine learning (ML) models, particularly DL architectures, have demonstrated a remarkable ability to distinguish between authentic cryptographic executions and those obfuscated with dummy power trace data, potentially rendering conventional hiding methods ineffective [13]. Figure 1: Workflow of a profiling-based side-channel attack using deep learning. This study systematically investigates the vulnerability of hiding techniques against DL-based SCAs (DL-SCAs). While conventional hiding techniques are designed to obscure power consumption and confound human-crafted analyses or basic statistical models, the findings of this study demonstrate that modern DL models can effectively bypass such defenses. By leveraging their capacity to extract subtle, high-dimensional patterns, DL models can distinguish between original and dummy traces with high accuracy, thereby compromising the intended obfuscation. To evaluate this vulnerability, a reproducible simulation framework was developed to generate realistic side-channel datasets, thereby enabling rigorous assessment of countermeasure robustness.