five

Packet Forwarding Control Protocol (PFCP) Intrusion Detection Dataset

收藏
DataCite Commons2023-04-25 更新2025-04-16 收录
下载链接:
https://ieee-dataport.org/documents/packet-forwarding-control-protocol-pfcp-intrusion-detection-dataset
下载链接
链接失效反馈
官方服务:
资源简介:
The advancements in the field of communication technology have resulted in an increasing demand for robust, high-speed, and secure connections and sessions between user equipment instances and the data network. The implementation of the newly defined 3GPP network architecture in the 5G core represents a significant leap towards fulfilling these demands. This architecture promises faster connectivity, low latency, higher data transfer rates, and improved network reliability. The 5G core has been designed to support a wide range of critical Internet of Things (IoT) and industrial use cases that require reliable end-to-end communication services. However, it has been observed that certain components and interfaces of the next-generation radio access network (NG-RAN) are susceptible to attacks that can compromise the network's ability to provide reliable communication services. The 5G core, too, has its share of inherent security flaws and protocol-specific weaknesses that can be exploited by attackers. Unfortunately, research into these core-related vulnerabilities has been limited in comparison to the extensive research done on the vulnerabilities of the NG-RAN. K3Y, in its efforts to investigate these issues, has focused on a set of attacks on the Packet Forwarding Control Protocol (PFCP) within the 5G core, in the context of the H2020 SANCUS project. It has been discovered that unauthorized session control packets transmitted to the network can disrupt established 5G tunnels without causing subscribers to lose their connectivity to the NG-RAN. This makes the detection of such attacks more difficult. The relevant Packet Capture (PCAP) files have been made available to the research community, and they can be used to train Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) that utilize Machine Learning (ML) and Deep Learning (DL) techniques. This dataset has the potential to make significant contributions to the field of network security and improve the overall reliability of 5G communication networks.
提供机构:
IEEE DataPort
创建时间:
2023-04-25
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作