## 2 Related Works  
### Side-Channel Attacks  
SCAs exploit unintended information leakage from cryptographic implementations [14; 15]. These attacks target physical characteristics of devices, such as power consumption, electromagnetic radiation, and timing variations, to extract cryptographic keys and other sensitive information. Unlike traditional cryptographic attacks, which focus on breaking mathematical structures, SCAs exploit vulnerabilities in the physical execution of cryptographic algorithms.  
Among the various SCA techniques, power analysis attacks, including Simple Power Analysis (SPA) and Differential Power Analysis (DPA), have proven to be particularly effective [16]. SPA relies on direct observation of power consumption patterns, while DPA employs statistical analysis to extract information from multiple power traces. Electromagnetic analysis and fault injection attacks further expand the range of SCAs, enabling adversaries to compromise cryptographic implementations with minimal access to the target system [17].  
Advancements in ML and DL have further elevated the risk posed by SCAs [18; 19]. Modern neural networks can process vast amounts of side-channel data, learning intricate patterns that allow attackers to derive cryptographic keys with higher efficiency than traditional statistical methods. The ability of DL models to generalize across different devices and encryption schemes presents an urgent need for robust and adaptive countermeasures against SCAs.  
Figure 2 presents the time-sequenced attack and defense perspectives in an SCA scenario. The attacker first collects power traces from cryptographic operations and trains a DL model to extract cryptographic keys. To counteract this, the defender employs a combination of techniques, starting with the generation of dummy data to introduce noise, followed by hiding and masking strategies, and finally, applying dynamic power management to further obfuscate the power consumption patterns. The outcome ensures that key extraction attempts fail, securing the system from DL-SCAs.  
One defining characteristic of SCAs in CPS is their non-invasive and non-disruptive nature. This characteristic underpins profiling-based SCAs, which follow a structured attack procedure. In a typical profiling attack scenario, an attacker first acquires a template device that is functionally equivalent to the deployed target, instruments it in a controlled environment, and records realistic side-channel data. A DL model is trained on this template device to learn the statistical mapping between side-channel leakages and secret-dependent internal states [20; 21].  
After training, the attack transitions from an active data collection phase to a passive exploitation phase. Once the model is trained, only passive power or electromagnetic measurements are required from the operational system. The pre-trained model can recover sensitive information, such as cryptographic keys or executed instructions, without software exploitation or observable process disturbance, rendering conventional monitoring mechanisms ineffective.  
This threat model is realistic in CPS, where commercial off-the-shelf microcontrollers and PLCs are widely reused. Furthermore, recent studies have shown that it is possible to analyze the behavior of a microcontroller during execution by using real-time traces [22].  
Figure 2: Time-sequenced side-channel attack and defense process.