# Detection Model for 5G Core PFCP DDoS Attacks Based on Sin-Cos-bIAVOa
## 5 Conclusions  
In this paper, we have focused on security issues in 5G core networks and proposed a DDoS attack detection model based on the Sin-Cos-bIAVOA algorithm, adapting Sin-Cos-bIAVOA from IoT DDoS detection to the 5G core network context. Through comparative experiments on a DDoS attack detection datasets for 5G core networks, the proposed detection model demonstrates superior performance across various attack scenarios, achieving higher detection accuracy than traditional DDoS detection algorithms. This provides a novel solution to the problem of DDoS attack detection in 5G core networks.  
The Sin-Cos-bIAVOA algorithm demonstrates high accuracy for PFCP DDoS binary classification attack detection in the 5G core network, achieving an average accuracy of 97.948% in balanced traffic attack detection, outperforming other conventional algorithms. In multiclass experiments, the proposed algorithm is capable of accurately classifying both normal traffic and various types of attack traffic. Experimental tests show that the Sin-Cos-bIVOA algorithm can reach an accuracy of 89.552% and specificity of 97.4019% under a 240-s timeout.  
This research indicates that a longer timeout allows the algorithm to more thoroughly process and analyze data, thereby unearthing more valuable feature information and enabling more precise classification and identification of attack types. However, in practical applications it is also necessary to consider the potential drawbacks of excessively long timeouts, such as increased resource consumption. Thus, selecting the most appropriate timeout setting based on specific scenario requirements is crucial in order to achieve an optimal balance between performance and resource utilization while effectively ensuring the security and stability of the network system. Further research will focus on performing more granular analyses of detection performance under various timeout conditions for different types of attacks as well as on exploring other factors that may influence algorithm performance, with the aim of continuously refining and improving the attack detection and classification algorithm to enhance its applicability and effectiveness in complex and dynamic cybersecurity environments.  
Future efforts will focus on further exploring and optimizing this detection model, enhancing its adaptability and accuracy, and improving the concurrent execution efficiency of DDoS attack detection in 5G core networks. This effort is aimed at addressing the new challenges posed to 5G core network security by complex application scenarios such as large bandwidth, low latency, and terminal roaming.  
Figure 11: Confusion matrices for the attack type classification results, with each subfigure representing experimental outcomes under different timeout conditions: (**a**) 15 s, (**b**) 20 s, (**c**) 60 s, (**d**) 120 s, (**e**) 240 s.  
**Author Contributions:** Conceptualization, Z.M.; methodology, Z.M.; software, Z.M.; validation, Z.M., R.Z. and L.G.; formal analysis, L.G.; investigation, L.G.; resources, L.G.; data curation, R.Z.; writing--original draft preparation, R.Z.; writing--review and editing, Z.M.; visualization, L.G.; supervision, L.G.; project administration, L.G.; funding acquisition, Z.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the University-Industry Collaborative Education Program of the Ministry of Education of China grant number 231101020130957.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are openly available in [IEEE Dataport] at [[http://doi.org/10.21227/e0](http://doi.org/10.21227/e0) gj-ev03]([http://doi.org/10.21227/e0](http://doi.org/10.21227/e0) gj-ev03) (accessed on 11 February 2025), reference number [32].  
**Conflicts of Interest:** The authors declare no conflicts of interest.