# Detection Model for 5G Core PFCP DDoS Attacks Based on Sin-Cos-bIAVOa
## 2 Related Work
### Relevant Intrusion Detection Datasets for Security Testing and Malware Prevention  
It is evident that intrusion detection mechanisms empowered with Artificial Intelligence (AI) models play a crucial role. To further investigate the characteristics of 5G networks, and in particular to evaluate the attack detection capabilities and effectiveness of AI models, researchers have conducted relevant studies focusing on 5G experimental platforms and datasets. The Vulnerability Assessment Approach (VAA) [31] is used to identify and evaluate vulnerabilities in 5G networks, particularly focusing on 5G Edge networks. The VAA utilizes the TOPSIS technique to analyze vulnerabilities from an attacker's perspective, considering the dynamic and scalable nature of 5G Edge networks. [PERSON] [32] led a team in designing a labeled security dataset named the \"5 GC PFCP Intrusion Detection Dataset\" as part of a cloud-based 5G Edge security testbed introduced to evaluate the VAA's accuracy, scalability, and performance. The dataset comprises network flow analytics designed for AI detection models to identify cyberattacks aimed at the Packet Forwarding Control Protocol (PFCP) utilized in the N4 interface of 5G core systems. It examines four distinct PFCP attack scenarios, offering crucial network traffic data through pcap files as well as detailed TCP/IP and application-layer metrics. The dataset consists of balanced TCP/IP and PFCP flow statistics, with an equal number of samples for each class, facilitating the training and testing of machine learning and deep learning algorithms for intrusion detection. They also proposed a containerized 5G testbed [33] designed to train defenders and develop intrusion detection systems by emulating various network functions and implementing specific attack scenarios, including full-stack attackstargeting both networked hosts and 5G network functions. The testbed generates malicious datasets containing traffic from both classical and 5G-specific attacks, which can be used to train AI and ML systems for proactive threat detection. [PERSON] et al. [34] explored the use of synthetic data generation for anomaly detection in the 5G NWDAF architecture. Due to the challenges of insufficient training data quality and quantity in NWDAF, they proposed using CTGAN to generate high-quality synthetic 5G NWDAF data. They then conducted experiments and evaluate the quality of the synthetic data and the resulting anomaly detection performance, finding that CTGAN-generated synthetic data can be a promising alternative to address data scarcity issues. Researchers have also utilized the NIDS datasets in [35] as a network testing platform to create synthetic datasets for public research. These datasets comprise labeled network flows constructed from features extracted from network traffic. 5G-NIDD [36] is a fully annotated dataset constructed on a fully functional 5G test network, and is available for researchers developing and testing AI/ML solutions. The test platform for the WUSTL-HDRL-2024 dataset [37] is designed to simulate a dynamic 5G network environment. This dataset meets the need to comprehensively capture various interactions and security threats within 5G networks. [PERSON], A. [38] proposed a framework design that integrates Exploratory Data Analysis (EDA) and Deep Learning (DL) for 5G network intrusion detection. Tests using the 5G-NIDD dataset demonstrated that the method possesses exceptional capabilities in both intrusion detection and attack identification. Researchers have employed various sampling methods to balance benign and malicious traffic in 5G networks; however, they often overlook the imbalance among different types of malicious traffic. CoSen-IDS [39] addresses this imbalance issue through cost-sensitive learning. The NetsLab-5 GORAN-IDD dataset [40] integrates both higher-layer network packet data and lower-layer radio telemetry, offering a comprehensive view of normal and malicious activity within a realistic 5G O-RAN deployment.