## 3 Experiments
### Experimental Analysis
#### 3.4.5 Actual Deployment Testing  
In recent years, the Internet of Things (IoT) has developed rapidly, bringing convenience to people. However, it also harbors numerous hidden risks, and security issues cannot be overlooked. In the future development and application of IoT, intrusion detection is worthy of attention and exploration. Through various experimental comparisons presented earlier, the theoretical superiority of our algorithm has been demonstrated. In the following sections, we will attempt to apply this algorithm to real-world scenarios and analyze its strengths and limitations under resource constraints or specific network conditions. Currently, the algorithm faces certain challenges when applied in different scenarios. Intrusion detection algorithms require high real-time capabilities in network environments, but traditional cloud-based intelligent controls fall short due to bandwidth limitations and delays, failing to meet the demands. Therefore, this study adopts an intelligent gateway controller based on edge computing to replace the traditional cloud-based framework, enabling the construction of an intrusion detection model. Edge computing provides services closer to smart devices at edge nodes, offering advantages like rapidity, real-time processing, low power consumption, and low bandwidth costs. Deploying the intrusion detection model at the edge can meet the real-time security needs of smart homes. In line \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Algorithm Model** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline Selvapandian & & & 96.28 & 94.41 & 97.51 & - \\ et al. [61] & Deep learning & & 95.40 & 94.81 & 97.25 & 96.01 \\ \hline [PERSON] & & & 98.81 & - & 97.25 & 99.43 \\ et al. [62] & & & 99.262 & 99.6350 & 99.22 & 99.43 \\ \hline [PERSON] & & & 99.12 & 80.33 & 98.38 & 88.45 \\ et al. [64] & XGBoost-DT & & & 98.34 & 85.52 & - & 90.94 \\ \hline [PERSON] & & & & 92.4 & - & 92.3 & 94.2 \\ et al. [65] & & & 98.70 & 99.50 & 98.39 & 98.94 \\ \hline [PERSON] & & & 96.26 & 89.00 & 89.00 & 89.00 \\ et al. [68] & & & 99.73 & 99.76 & 99.66 & 99.48 \\ \hline Algorithm of this paper & & & 99.66 & 99.89 & 99.09 & 99.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Results of the comparison of the proposed model with the state-of-the-art methodology. with the \"cloud-edge-device\" model, a technological system combines edge computing, IoT, cloud computing, and big data technologies. This results in a three-dimensional, efficient intrusion detection and multi-device networking capability, forming an intelligent IoT system framework, as depicted in Figure 12. The framework enables real-time data aggregation and management, enhancing the security measures for IoT. In the above system framework, the research aims to enable the cloud server to receive instructions from administrators or users and relay these commands to the intelligent gateway of edge computing through the cloud server. Serving as the central hub of all information, the intelligent gateway controls commands from the cloud server. It receives sensor information from the device side or transmits control commands to the terminal controller. The intrusion detection algorithm is applied to identify and detect the status of the information, determining whether it is in a normal state. In different application scenarios, the number of devices in the intelligent gateway may vary. For this study, we have chosen Allwinnertech H6 as the core SoC of the edge intelligent gateway platform. This chip has a quad-core Cortex-A53 CPU and Mali T720 GPU, supporting dual-channel DDR4 and EMMC5.0 high-speed flash memory. Such selection enables us to better test the algorithm's efficiency and versatility under limited computational resources. Within the framework of edge computing, implementing an intrusion detection system heavily relies on the data capture module to acquire flow data and parse data packets.