Improved dropping attacks detecting system in 5g networks using machine learning and deep learning approaches

Non Orthogonal Multiple Access (NOMA) successfully drew attention to the deployment of 5th Generation (5G) wireless communication systems, and it is now considered a significant technology in 5G communications. The primary enhancement in 5G is the speed, which may be 100 times faster than 4G. Due to the rising number of internal or external attacks on the Network, wireless intrusion detection systems are a vital aspect of any system connected to the Internet, and 5G will demand considerable improvements in data rate and security. In this paper, we have built a simulator for NOMA and applied a dropping attack to extract a dataset from the simulation model. The accuracy for detecting dropping attacks using the extracted data after applying ML algorithms was 95.7% for LR. Furthermore, this work suggests a methodology for wireless cyberattack detection in 5G networks based on applying several ML and DL techniques such as Decision Trees, KNN, Multi-class Decision Jungle, Multi-class Decision Forest, and Multi-class Neural Network. The proposed work is implemented and tested using a comprehensive Wi-Fi network benchmark dataset. The conducted experiments resulted in an outstanding performance with an accuracy of 99% for the KNN algorithm and 93% for DF and Neural Network.

Automated summary

This paper introduces the Non Orthogonal Multiple Access (NOMA) technology in 5G communications and proposes a methodology for wireless cyberattack detection in 5G networks using various machine learning and deep learning techniques. The simulation and experiments show high accuracy rates for detecting dropping attacks and achieving outstanding performance with the KNN algorithm, Decision Forest, and Neural Network.