## 7 Conclusions and Future Work  
It is very difficult to process stream traffic data and detect security threats in a particular network [31]. In addition, it involves complex procedures to analyze traffic data in a 5G network environment. Therefore, research on DDoS attacks in the 5G network environment has a high barrier to entry. In the existing 5G cellular landscape, the trend of malicious DDoS events can be broadly categorized into two. The one pertains to any DDoS attack that abuses the control protocol used in the RAN, generally targeting a wireless base station by compromising the RRC protocol it uses in the RAN. The other pertains to any DDoS attack that exploits the control protocol used for voice services. Despite the fact that a lot of research has been conducted to cope with these two categorized events, there was no research exploring the applicability of AI-based ML against DDoS attacks targeting the CN, not the RAN or voice service network. In this sense, the current research proposes a model capable of ML-based detection of signaling DDoS attacks targeting the 5G CN and evaluates its performance. Out of several ML-based detecting models, SVM and random forest were found to show the highest accuracy. However, it was found that all algorithms of Bayesian, random forest, and SVM have high training time. The experimental result of this study showed that, out of the ML algorithms used for the detection of signaling DDoS, the random forest perspective leads to outcomes having stable and high performance, less time consumption, and high accuracy. As a result of diversely constructing features for the random forest hyperparameters, it could be verified that, when increasing the value of a feature (n_estimator) that designates the number of decision trees as well as the maximum depth of each decision tree, the detection accuracy is enhanced, the average of accuracy scores from five times of cross-validation is increased, and the error range is decreased. The data used in this work were not simply generated by simulators, but actual traffic data in mobile carriers operating 5G networks, and there were many restrictions on obtaining it due to various strict corporate policies, such as affecting operational services. Nevertheless, we think it was meaningful that the test was conducted with this amount of data. Since the system is currently being tested by installing it on the test network of mobile carriers [28], more data will be secured in the future, and the results will be analyzed and disclosed as follow-up work. In this study, the goal was to collect, preprocess, and learn traffic to detect DDoS targeting 5G core networks. However, we will advance this study, create an efficiency table that compares all ML algorithms using a wide range of algorithms, and push for the next step of research on which algorithms are best. In the future, we are planning to make the most of deep learning in detecting signaling DDoS, and comparing the resulting performance with that when utilizing ML classifiers, taking into account the detection accuracy and even the duration of time that each takes until trained and detecting DDoS. If we apply a deep learning algorithm to detect signal DDoS in a 5G network environment, we are also considering using dynamic features [32]. In addition, the SBA and EBA techniques used in this paper will be applied in various ways. When implementing new techniques, faster and better performance technologies should be used [33]. In the next work, we will not only use more diverse machine learning algorithms but also use more diverse features.