## 3 Experiments
### Experimental Analysis
#### 3.4.5 Actual Deployment Testing
Simultaneously, the data processing module aids in preprocessing flow data to conform to the model's input requirements. Lastly, the intrusion detection module classifies and detects the flow, presenting the detection results. The specific deployment process design is illustrated in Figure 13.  
After completing the deployment, we conducted real-world testing and a comprehensive evaluation of the deployed model using the multiple evaluation criteria described earlier. In the Internet of Things (IoT) network environment, Denial-of-Service (DoS) attacks are currently one of the most common and highly destructive attacks. These attacks overwhelm the target by flooding it with many data packets, causing excessive resource consumption and rendering the service unavailable. To assess the algorithm's ability to detect such attacks, we chose the XOIC tool and utilized UDP and ICMP messages as the two DoS attack methods for testing. Within the IoT framework described in the document, end-users issue control commands through the cloud server, which then relays the information to the edge intelligent gateway device. At the gateway, the information is subjected to real-time detection. Considering practical constraints, each normal connection was repeated ten times, lasting for 60 s. This comprised seven connections between the cloud server and the edge intelligent gateway and three connections between terminal devices and the edge intelligent  
Figure 12: Smart iot intrusion detection system framework. gateway. Subsequently, the two attack types were initiated, each lasting for 10 s. This process was repeated 100 times, and the average values were calculated based on the actual detection results. Moreover, to minimize interference from other traffic passing through the gateway, we set up filtering rules in the data collection and analysis module. Specifically, we collected and analyzed attack traffic between attacking machines and the target and normal traffic between the intelligent gateway and the servers or terminal devices. Throughout the testing process, we observed the statistical information and logs from the local detection module to analyze the data. The actual intrusion detection results based on our proposed algorithm are presented in Table 21.  
In the edge intelligent gateway application with limited computing resources, we implemented an enhanced model to perform feature engineering based on Minimum Redundancy, Maximum Relevance and Synthetic Minority Over-sampling Technique. This resulted in a reduction of data space complexity and a decrease in computational burden. Compared to the unprocessed algorithm, the improved algorithm demonstrated a decrease of 0.404 s in testing time under similar network conditions. This enhancement enables the algorithm to handle a greater volume of traffic data within constrained computing resources and time limitations, meeting the intrusion detection system controller's requirements for identifying network data flows. Furthermore, the improved algorithm exhibited notable improvements in accuracy, precision, recall, and F-measure values, with increases of 4.15%, 2.80%, 3.68%, and 3.24%, respectively, leading to more accurate data classification. Based on the aforementioned test results, deploying the enhanced intrusion detection model on edge nodes enables real-time monitoring of traffic data. It allows timely response to intrusion behavior, effectively safeguarding the security of the IoT platform. However, to further enhance the algorithm's robustness and adaptability, we should focus on providing \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Catboost** & **Algorithm of This Paper** \\ \hline Accuracy/\% & 94.5992 & 98.7448 \\ Precision/\% & 96.4023 & 99.2053 \\ Recall/\% & 95.1788 & 98.8556 \\ F-measure/\% & 95.7866 & 99.0301 \\ Test time/s & 0.772 & 0.368 \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Comparison of actual application before and after algorithm improvement. Figure 13: Smart IoT Intrusion Detection System Realization Process.