# Anomaly Detection in 6G Networks Using Machine Learning Methods
###### Abstract
A unique IDS model was offered to overcome these issues in order to increase accuracy and decrease FAR. This model incorporates both SIDS and AIDS. In contrast to AIDS, SIDS might identify frequent intrusions [16]. Data mining techniques are employed in intrusion detection (ID), a data analysis method, to automatically find, extract, and differentiate between normal and invasive patterns. DM problems typically fall into one of four categories: classification, grouping, regression, or association rule learning. A crucial step in the IDS process is the feature selection (FS) technique, which identifies the important features and eliminates the useless ones to reduce performance degradation [17]. The correlation-based heuristic assessment function used by correlation FS (CFS) allows users to prioritize features. It contrasts the attribute vector subsets that are tied to class labels but not to one another. The CFS algorithm mandates excluding features that have little relevance to the class. Due to their frequent connections to one or more of the other aspects, excess features should be looked into [18]. Classification, grouping, regression, and association rule learning are the four general areas in which DM problems can be categorized. The FS technique, a critical stage in the IDS process, detects the critical features and eliminates the unimportant ones to prevent performance degradation [19]. Users can order characteristics according to their importance using CFS's correlation-based heuristic assessment function. It contrasts attribute vector subsets connected to class labels but not to one another. Features barely relevant to the class must be excluded, according to the CFS algorithm. Excessive features should be investigated due to their frequent linkages to one or more of the other aspects [20]. Ingenious mitigation techniques, proactive threat identification, and self-sustaining networks in 6G are all required to automate forthcoming networks. Determining and responding to possible attacks based on network abnormalities rather than cryptographic approaches requires an end-to-end security strategy that uses AI techniques. The security requirements and difficulties for 6G applications are listed in Table 1. This research will investigate how AI can help 6G networks be more secure. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Challenges in Security** & **6G Application** & **Requirements for Security** \\ \hline \multirow{4}{*}{[21]} & • Resources are scarce. & \multirow{4}{*}{Holographic telepresence} &  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Challenges in Security** & **6G Application** & **Requirements for Security** \\ \hline
[22, 23] & \(\bullet\) & High altitude, high & & \(\bullet\) & A range of gadgets. \\
[22, 23] & & movement, limited energy, & & \(\bullet\) & Real-time operations with lower operating expenses. \\
[22, 23] & & and a variety of equipment & & \(\bullet\) & High scalability. \\
[22, 23] & & are all factors. & & \(\bullet\) & Design of an end-to-end \\
[22, 23] & \(\bullet\) & Attacks by terrorists. & & \(\bullet\) & A security edges. \\
[4] & \(\bullet\) & Physical interference. & & \(\bullet\) & A security edges. \\
[24] & \(\bullet\) & A lack of security & & \(\bullet\) & Minimal privacy. \\
[24] & \(\bullet\) & regulations. & & \(\bullet\) & Operation in real-time. \\
[24] & \(\bullet\) & Attacks that include physical & & \(\bullet\) & \(\bullet\) \\  & & meddling; scarcity of & & \(\bullet\) & \(\bullet\)  
The world of technology is shifting toward hardware and simple algorithms, IoE, and complex networking. Although there are ongoing studies, intrusion detection systems are unable to optimize their detection rate (DR), false alarm rate (FAR), false negative rate (FNR), false positive rate (FPR), or execution time due to the huge dimensions of the standard dataset and the incidence of zero-day assaults. Even though it directly affects resources, time complexity has not been acknowledged as a significant component.