# Anomaly Detection in 6G Networks Using Machine Learning Methods
###### Abstract
In order to extract the best subset of the original features, dimensionality reduction with an FS is reimplemented in this study. The accuracy and stability of the intrusion detection systems are then increased while minimizing the necessary calculation time by forwarding the reimplemented hybrid EL to these subsets. The reimplemented process develops hybrid EL and FS algorithms to obtain precise and effective IDs. In the reimplemented approach, an anomaly message is detected and classified as spam or ham using novel AD6 GNs based on EL for wireless communication networks. ML approaches substantially contribute to detecting spam tweets, images/videos, and SMS messages on mobile devices. An IDS guards computer networks against malicious invasions and is used to check for network weaknesses. For network analysis, the three main classifications of an intrusion detection system are signature-based, anomaly-based, and hybrid-based. ML methods significantly aid in detecting various intrusions on host and network systems. The main contributions of this research are as follows:  
1. Within the FS framework, a unique CFS-RF method is utilized to evaluate the correlation of the chosen features. Improving the efficiency of the testing and training processes is quite advantageous. 2. The effectiveness of the multi-class and binary forms will be enhanced and used on the three unbalanced datasets. The process explains hybrid ensemble algorithms by modifying two different classifiers to function as adaboosting, followed by the average voting technique (bagging method) to combine judgments from several classification judgments using an ensemble of classifiers, such as SVM and RF. 3. This article investigates how AI may be used in 6G security to strengthen and reinforce safeguarding 6G networks. This study reimplements a novel AD6 GN using EL to do this. The remainder of this paper is organized as follows: Many similar works are presented in Section 2. In Section 3, the basics of threats and attacks are discussed. At the same time, Section 4 AI/ML technology in the 6G network is discussed. Section 5 offers a thorough explanation of the methodology and the reimplemented system. Section 6 discusses various ML techniques, shows how the reimplemented system was put into practice using the datasets that were used, and presents the discussion and analysis of the results of this and other studies. Section 7 ends with a summary of the findings and recommendations for additional investigation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Challenges in Security** & **6G Application** & **Requirements for Security** \\ \hline \multirow{8}{*}{[35; 36; 37]} & \(\bullet\) & Security of the digital model as well as the & & \(\bullet\) & Broadband use. \\  & & physical model. & & \(\bullet\) & Superb privacy. \\  & & \(\bullet\) & A range of gadgets. & & \(\bullet\) & Secure lightly. \\  & & Privacy-preserving. & & \(\bullet\) & Scalability. \\  & & \(\bullet\) & Very good mobility. & & \(\bullet\) & Flexible security measures. \\  & & \(\bullet\) & Separate security measures. & & \(\bullet\) & Robustness. \\  & & \(\bullet\) & Design of the building; & & \(\bullet\) & Accessibility \\  & & physical assaults. & & Brain–computer connections that & \(\bullet\) & Integrity \\  & & \(\bullet\) & Privacy issues. & & \(\bullet\) & Safety \\  & & \(\bullet\) & Systems for total security. & & \(\bullet\) & Confidentiality. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._