# Research on a PSO-H-SVM-Based Intrusion Detection Method for Industrial Robotic Arms
## 4 Experiments and Evaluation of Results
### Analysis of Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Classifier** & **C** & **Gamma** & **Accuracy** \\ \hline SVM & classifier1 & 1.0 & ’auto’ & 93.87\% \\ \hline PSO-SVM & classifier1 & 7.5592 & 0.0256 & 95.04\% \\ \hline \multirow{4}{*}{PSO-H-SVM} & classifier1 & 8.8983 & 0.0796 & \\ \cline{2-5}  & classifier2 & 6.1910 & 0.0941 & \\ \cline{1-1} \cline{2-5}  & classifier3 & 9.2709 & 0.0707 & \\ \cline{1-1} \cline{2-5}  & classifier4 & 9.5290 & 0.0214 & \\ \hline \hline \end{tabular}  
* The Classifier item in the table represents the number of classifiers constructed by each algorithm.  
\end{table} 
Table 2: Hyperparameter values and accuracy of each algorithm. Figure 7: Confusion matrix of classification results. Finally, we tested the physical process logic intrusion detection method on the physical experiment platform, and used two metrics to measure the effectiveness of the intrusion detection method throughout the testing process [50]. The first metric is the accuracy rate, which is the sum percentage of the number of correct judgments of the normal state and the attack state with the total number of tests; the second metric is the false detection rate, which refers to the percentage of the number of times the normal state is incorrectly judged as the attack state over the total number of times the normal state is judged. The expressions of the accuracy rate and the false detection rate are as follows:  
\[\text{ACC}= \frac{\text{NN}+\text{AA}}{\text{Tnum}} \tag{12}\] \[\text{FAR}= \frac{\text{NA}}{\text{NN}+\text{NA}} \tag{13}\]  
where, ACC is the accuracy rate; FAR is the false detection rate; NN is the number of times the normal state is judged correctly; AA is the number of times the attack state is judged correctly; NA is the number of times the normal state is judged as the attack state; and Tnum is the total number of tests. In the test experiment, the intrusion behavior of the attacker was simulated at the ROS control terminal, and joint commands with abnormal physical processes were issued to the robotic arm while performing the attack. For steps 1-6 of the closure operation presented in Section 3.3, the possible attacks on the system are given in Table 3. For the previously collected 10 paths, normal operations and attack experiments were conducted, resulting in a total of 100 sets of experiments, of which 90 are sets of the robotic arm performing normal instructions and 10 are sets of attack behavior. The experimental results were that 83 groups were judged as normal behavior, of which 81 groups were judged correctly and 2 groups were judged as normal behavior despite being attack behavior; 17 groups were judged as attack behavior, of which 8 groups were judged correctly and 9 groups were judged as attack behavior despite being normal behavior. According to Equations (12) and (13), the accuracy rate of this intrusion detection method can be calculated as 90% and the false detection rate as 10%. In the event of an attack, the terminal will prompt the operator to detect an abnormality and display the joint state value that maintains the current position, at which time the system is not powered down and the operator can intervene in the robotic arm system to take the next protective measures. In summary, the accuracy of the PSO-H-SVM classification model has reached 96.02%, and the classification accuracy of each class of states based on the confusion matrix can be observed to be more than 95%. For physical process logic attacks, the detection accuracy of the intrusion detection method reaches 90%, which can effectively demonstrate that the intrusion detection method based on the PSO-H-SVM state classification model and response mechanism can effectively detect the physical process logic attack against the robotic arm and mitigate the damage caused by the attack.