Detecting IoT Botnet in 5G Core Network Using Machine Learning
Source abstract
Abstract
As Internet of Things (IoT) devices with security issues are connected to 5G mobile networks, the importance of IoT Botnet detection research in mobile network environments is increasing. However, the existing research focused on AI-based IoT Botnet detection research in wired network environments. In addition, the existing research related to IoT Botnet detection in ML-based mobile network environments have been conducted up to 4G. Therefore, this paper conducts a study on ML-based IoT Botnet traffic detection in the 5G core network. The binary and multiclass classification was performed to compare simple normal/malicious detection and normal/three-type IoT Botnet malware detection. In both classification methods, the IoT Botnet detection performance using only 5GC’s GTP-U packets decreased by at least 22.99% of accuracy compared to detection in wired network environment. In addition, by conducting a feature importance experiment, the importance of feature study for IoT Botnet detection considering 5GC network characteristics was confirmed. Since this paper analyzed IoT botnet traffic passing through the 5GC network using ML and presented detection results, think it will be meaningful as a reference for research to link AI-based security to the 5GC network.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Internet Traffic Analysis and Secure E-voting · Advanced Data and IoT Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (29)
Show source-provided references
1. A survey of secure internet of things in relation to blockchain
2. Sequential model based intrusion detection system for IoT servers using deep learning methods10.3390/s21041113 ↗
3. Internet of things botnet detection approaches: Analysis and recommendations for future research10.3390/app11125713 ↗
4. DNNBIoT: Deep neural network-based botnet detection and classification
5. Machine learning DDoS detection for consumer internet of things devices
6. 5G core network security issues and attack classification from network protocol perspective
7. Why would we get attacked? an analysis of attacker’s aims behind DDoS attacks
8. Blockchain security in cloud computing: Use cases, challenges, and solutions10.3390/sym9080164 ↗
9. Bot detection by friends graph in social networks
10. Securing future internet and 5G using customer edge switching using DNSCrypt and DNSSEC
11. Security of 5G-mobile backhaul networks: A survey
12. Yang · ETSI Security Week 2018 · 2018 · 1
13. 3GPP, “Security architecture and procedures for 5G system,” 3GPP TS 33.501 v17.4.1, Jan. 2022.
14. 3GPP, “System System architecture for the 5G system (5GS),” 3GPP TS 23.501 v17.3.0, Dec. 2021.
15. J. Wu, R. Li, X. An, C. Peng, Z. Liu et al., “Toward native artificial intelligence in 6G networks: System design, architectures, and paradigms,” arXiv preprint arXiv:2103.02823, pp. 1–7, 2021.
16. Medbiot: Generation of an IoT botnet dataset in a medium-sized IoT network
17. 3GPP, “General packet radio service (GPRS); GPRS tunneling protocol (GTP); across the Gn and Gp interface,” 3GPP TS 29.060 v17.1.0, Sep. 2021.10.1007/978-3-642-27739-9_1522-1 ↗
18. Open, programmable, and virtualized 5G networks: State-of-the-art and the road ahead10.1016/j.comnet.2020.107516 ↗
19. Intelligent and autonomous management in cloud-native future networks-a survey on related standards from an architectural perspective10.3390/fi13020042 ↗
20. 5G evolution: A view on 5G cellular technology beyond 3GPP release 1510.1109/ACCESS.2019.2939938 ↗
21. “Netmanias, “5G traffic flow,” [Online]. Available: https://www.netmanias.com/en/post/oneshot/14104/5g/5g-traffic-flow, 2019.
22. N. A. Stoian, “Machine learning for anomaly detection in IoT networks: Malware analysis on the IoT-23 data set,” B.S. thesis, University of Twente, Netherlands, 2020.
23. A deep learning ensemble for network anomaly and cyber-attack detection10.3390/s20164583 ↗
24. A Multi-class neural network model for rapid detection of IoT botnet attacks
25. Machine learning based IoT-botnet attack detection with sequential architecture10.3390/s20164372 ↗
26. Towards a universal features set for IoT botnet attacks detection
27. K. Saeedi, “Machine learning for DDoS detection in packet core network for IoT,” M.S. thesis, Dept. Computer Science, Eletrical and Space Engineering, Luleå University of Technology, Swedish, 2019.
28. 5G NB-IoT: Efficient network traffic filtering for multitenant IoT cellular networks10.1155/2018/9291506 ↗
29. Fraunhofer FOKUS, “Open5Gcore, fundamental 5g core network functionality,” [Online]. Available: https://www.open5gcore.org/, 2021.