Biblio

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.

Currently, research on 5G communication is focusing increasingly on communication techniques. The previous studies have primarily focused on the prevention of communications disruption. To date, there has not been sufficient research on network anomaly detection as a countermeasure against on security aspect. 5g network data will be more complex and dynamic, intelligent network anomaly detection is necessary solution for protecting the network infrastructure. However, since the AI-based network anomaly detection is dependent on data, it is difficult to collect the actual labeled data in the industrial field. Also, the performance degradation in the application process to real field may occur because of the domain shift. Therefore, in this paper, we research the intelligent network anomaly detection technique based on domain adaptation (DA) in 5G edge network in order to solve the problem caused by data-driven AI. It allows us to train the models in data-rich domains and apply detection techniques in insufficient amount of data. For Our method will contribute to AI-based network anomaly detection for improving the security for 5G edge network.

The 5G technology will play a crucial role in global economic growth through numerous industrial developments. However, it is essential to ensure the security of these developed systems, while 5G brings unique security challenges. This paper contributes explicitly to the need for an effective Vulnerability Assessment Approach (VAA) to identify and assess the vulnerabilities in 5G networks in an accurate, salable, and dynamic way. The proposed approach develops an optimized mechanism based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to analyze the vulnerabilities in 5G Edge networks from the attacker perspective while considering the dynamic and scalable Edge properties. Furthermore, we introduce a cloud-based 5G Edge security testbed to test and evaluate the accuracy, scalability, and performance of the proposed VAA.