# Anomaly Detection in 6G Networks Using Machine Learning Methods
###### Abstract  
While the cloudification of networks with a micro-services-oriented design is a well-known feature of 5G, the 6G era of networks is closely related to intelligent network orchestration and management. Consequently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) have a big part to play in the 6G paradigm that is being imagined. Future end-to-end automation of networks requires proactive threat detection, the use of clever mitigation strategies, and confirmation that 6G networks will be self-sustaining. To strengthen and consolidate the role of AI in safeguarding 6G networks, this article explores how AI may be employed in 6G security. In order to achieve this, a novel anomaly detection system for 6G networks (AD6 GNs) based on ensemble learning (EL) for communication networks was redeveloped in this study. The first stage in the EL-ADCN process is pre-processing. The second stage is the feature selection approach. It applies the reimplemented hybrid approach using a comparison of the ensemble learning and feature selection random forest algorithms (CFS-RF). NB2015, CIC_IDS2017, NSL KDD, and CICDDOS2019 are the three datasets, each given a reduced dimensionality, and the top subset characteristic for each is determined separately. Hybrid EL techniques are used in the third step to find intrusions. The average voting methodology is employed as an aggregation method, and two classifiers--support vector machines (SVM) and random forests (RF)--are modified to be used as EL algorithms for bagging and adaboosting, respectively. Testing the concept of the last step involves employing classification forms that are binary and multi-class. The best experimental results were obtained by applying 30, 35, 40, and 40 features of the reimplemented system to the three datasets: NSL_KDD, UNSW_NB2015, CIC_IDS2017, and CICDDOS2019. For the NSL_KDD dataset, the accuracy was 99.5% with a false alarm rate of 0.0038; the accuracy was 99.9% for the UNSW_NB2015 dataset with a false alarm rate of 0.0076; and the accuracy was 99.8% for the CIC_IDS2017 dataset with a false alarm rate of 0.0009. However, the accuracy was 99.95426% for the CICDDOS2019 dataset, with a false alarm rate of 0.00113.  
anomaly detection; 6G networks; machine learning; ensemble approach; bagging algorithm; correlation feature selection algorithm 2023123330 develop over the next ten years [2; 3; 4; 5]. After 2030, networks will need to be equipped with cutting-edge technologies that allow connected intelligence in digital virtual worlds in order to address networking and communication issues. According to published research, 6G systems will have new application fields, such as wireless brain-computer interfaces (BCI), networked robotics and autonomous systems, and extended multimodal reality (XR) applications [6]. At the same time, traditional applications, such as multimedia streaming, will continue to exist. Other 6G use cases [7] that demand high data throughput, incredibly low latencies, and incredibly reliable networks include holographic telepresence, eHealth, and in-body networks. Identifying zero-day attacks is a difficult issue. Many suspicious actions are discovered every day. The effects of these complex intrusions can develop into significant dangers that make it more challenging for current intrusion detection systems (IDSs) [8; 9; 10; 11; 12; 13]. IDSs send out alerts when they find unusual activity or an identified threat. Every negative act that compromises the information system is considered an incursion [14]. IDSs watch computer systems for any unusual activity that a typical packet filter would miss. They look for indicators of potentially dangerous behavior, cyber resistance to disruptive activities, and unauthorized access to the system in network packets. The two techniques employed by IDSs to identify intrusions are anomaly intrusion detection systems (AIDS) and signature intrusion detection systems (SIDS) [15]. AIDS is flawed, and it has a high percentage of false alarms.