## 5 Experimental Results
### Experimental Setup  
The experimental setup, as shown in Figure 9a, establishes a testing platform for lightweight IoT devices to simulate and evaluate the encryption key protection mechanism and its ability to resist SCAs. In this experiment, an Arduino UNO was used as the client device for data transmission and was set as the target for SCAs. The server side was implemented by Java, responsible for receiving and verifying data from the client to ensure the integrity and correctness of the transmission. The attack platform was configured based on the methods proposed in [31], with both hardware and software set up accordingly. During the attack, the H-Field probe used in this study was the CW505  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**IoT Device (A)** & **Public Information** & **Server (B)** \\ \hline Private Key Selection & & Private Key Selection \\ a = 777 & \(G=37\) & \(\text{b}=888\) \\ Public Key Calculation & & Public Key Calculation \\ \(PK_{A}=G^{\text{a}}\) mod \(P\) & & \(PK_{B}=G^{\text{b}}\) mod \(P\) \\ \(=37^{\prime\prime}mod\)\(2,147,483,647\) & & \(=37^{\prime 888}\)_mod_\(2,147,483,647\) \\ \(=\) “\(1,348,037,377\)” & & \(=\) “\(37,387,895\)” \\ Public Key Exchange & & Public Key Exchange \\  & \(PK_{A}=\) “\(1,348,037,377\)” & \\  & \(PK_{B}=\) “\(37,387,895\)” & \\ Shared Secret Calculation & & Shared Secret Calculation \\ \(SK=PK_{B}\)\({}^{a}\)_mod \(P\)_ & & \(SK=PK_{A}\)\({}^{b}\)_mod_\(P\) \\ \(=37,387,895\)”\(7^{\prime\prime}\)_mod \(2,147,483,647\)_ & & \(=1,348,037,377^{\prime 688}\)_mod_\(2,147,483,647\) \\ \(=\) “\(1,142,936,476\)” & & \(=\) “\(1,142,936,476\)” \\ \hline \hline \end{tabular}
\end{table}
Table 4: Lightweight D-H key exchange process for IoT devices.  
Planar H-Field Probe, manufactured by NewAE Technology Inc. This probe is specifically designed for side-channel analysis and is capable of capturing electromagnetic radiation emitted by the target device during operations such as AES encryption. The CW505 probe was positioned near the client device to monitor and collect the electromagnetic signals generated during the encryption process, providing critical data for analyzing potential side-channel vulnerabilities. These signals were recorded as trace data for further analysis. Figure (b)b provides a physical image of the electromagnetic signals being captured via a loop antenna, which were processed in real time using the PicoScope 5244B oscilloscope. The captured traces were transmitted to the control computer for formatting and preprocessing, and subsequently sent to the computing server for further analysis. The server ran a CPA program, following the configuration by [PERSON] et al. [32], to conduct attack analysis and return results. This setup effectively simulates real-world SCA scenarios in an IoT environment and validates the feasibility and effectiveness of the proposed experimental methodology. Specifically, the CPA uses Pearson's correlation coefficient to determine the linear relationship between the hypothetical power consumption values (e.g., Hamming Weight or Hamming Distance models) and the recorded side-channel traces. The correlation coefficient is calculated using Equation (1). Here \(r_{j,k,t}\) represents the correlation coefficient for the \(j-th\) subkey, the \(kth\) key guess, and the \(t-th\) sample point of the trace. \(x\) refers to the hypothetical power values, \(y\) refers to the recorded side-channel signal, and \(\overline{x},\overline{y}\) are their respective averages. By analyzing the resulting correlation matrix \(R_{j}\), the element with the highest correlation value identifies the correct subkey.  
Figure 9: _Cont._