5G is a standard that proposed to overcome the limitations of the 4G network. Massive broadband and very low latency are expected to be achieved on the 5G network. Cloud computing is one of the critical technologies to enable 5G to handle enormous broadband. However, the cloud server is usually placed far away from the user so that delay, congestion, or the other issues may occur. To solve this problem, Multi-access Edge Computing (MEC) is a cloud computing paradigm that brings computing, storage, and network resources adjacent to the users. Supporting a massive number of devices connected in 5G also opens an excellent opportunity for attacks such as Denial of Service (DoS). DoS remains an issue in term of performance improvement. In this paper, we intend to introduce a new system that leveraging the MEC function and integrate it with Software
Defined Network (SDN) and supervised decision tree learning approaches which are C5.0, Bagging-CART (B-CART), and Random Forest (RF).
Our simulation lab collects data which consist of three types of DoS attacks:
ICMP echo flood, TCP Xmas flood, and UDP flood attack. Evaluation conducted based on the collected information shows that the selected decision tree learning approaches perform better than the other machine learning methods in terms of accuracy and processing time. It indicates that the proposed supervised learning is suitable for DoS detection system. Finally, the overall result shows that the proposed scheme can enable the intrusion detection function in MEC 5G to detect and mitigate the DoS attack effectively.

5G is a standard that proposed to overcome the limitations of the 4G network. Massive broadband and very low latency are expected to be achieved on the 5G network. Cloud computing is one of the critical technologies to enable 5G to handle enormous broadband. However, the cloud server is usually placed far away from the user so that delay, congestion, or the other issues may occur. To solve this problem, Multi-access Edge Computing (MEC) is a cloud computing paradigm that brings computing, storage, and network resources adjacent to the users. Supporting a massive number of devices connected in 5G also opens an excellent opportunity for attacks such as Denial of Service (DoS). DoS remains an issue in term of performance improvement. In this paper, we intend to introduce a new system that leveraging the MEC function and integrate it with Software
Defined Network (SDN) and supervised decision tree learning approaches which are C5.0, Bagging-CART (B-CART), and Random Forest (RF).
Our simulation lab collects data which consist of three types of DoS attacks:
ICMP echo flood, TCP Xmas flood, and UDP flood attack. Evaluation conducted based on the collected information shows that the selected decision tree learning approaches perform better than the other machine learning methods in terms of accuracy and processing time. It indicates that the proposed supervised learning is suitable for DoS detection system. Finally, the overall result shows that the proposed scheme can enable the intrusion detection function in MEC 5G to detect and mitigate the DoS attack effectively.

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