| Title | AI-based Network Security Enhancement for 5G Industrial Internet of Things Environments |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Lee, Jonghoon, Kim, Hyunjin, Park, Chulhee, Kim, Youngsoo, Park, Jong-Geun |
| Conference Name | 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) |
| Keywords | 5G Edge security, 5G mobile communication, 5G Model Factory, 5G Network Security, AI-based Intrusion Detection, composability, compositionality, intelligent data, Intelligent Network Intrusion Detection, Intelligent networks, network intrusion detection, Network security, performance evaluation, Production facilities, pubcrawl, resilience, Resiliency, Scalability, security, System performance |
| Abstract | The recent 5G networks aim to provide higher speed, lower latency, and greater capacity; therefore, compared to the previous mobile networks, more advanced and intelligent network security is essential for 5G networks. To detect unknown and evolving 5G network intrusions, this paper presents an artificial intelligence (AI)-based network threat detection system to perform data labeling, data filtering, data preprocessing, and data learning for 5G network flow and security event data. The performance evaluations are first conducted on two well-known datasets-NSL-KDD and CICIDS 2017; then, the practical testing of proposed system is performed in 5G industrial IoT environments. To demonstrate detection against network threats in real 5G environments, this study utilizes the 5G model factory, which is downscaled to a real smart factory that comprises a number of 5G industrial IoT-based devices. |
| Notes | ISSN: 2162-1241 |
| DOI | 10.1109/ICTC55196.2022.9952490 |
| Citation Key | lee_ai-based_2022 |
AI-based Network Security Enhancement for 5G Industrial Internet of Things Environments