# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 2 Related Work  
While research on the security of sixth-generation (6G) wireless communication systems has already started to give smooth connectivity to the growing number of users and to improve ML services, the 5G network systems are still in the process of being fully standardized. The problem of system secrecy rate optimization has already been extensively studied using a variety of contemporary methodologies. The authors in [41] used an IRS-assisted SWIPT system with a user-equipment (UE)-installed power-splitting (PS) scheme to study the physical-layer security and transmission optimization issues. It is crucial to jointly determine the lowest collected energy and maximum transmitter power required to maximize the system secrecy rate, as well as the transmitter power, the UE's PS factor, and the IRS's phase shift matrix [42]. They suggested utilizing an alternating optimization (AO)-based approach to get the optimum answers. The authors' major goal in [43] was to provide the most recent developments in DL-based physical layer techniques in order to open the door for exciting 6G applications. In [44], the authors outline their vision for scalable, reliable edge AI systems that merge wireless communication plans with distributed machine learning models. An intrusion detection system for networks, an ensemble based on decision trees and rule learners, was the idea put forth by the authors in [45]. Using the NSL KDD dataset for IDSs with erratic base classifiers, a unique DAR ensemble architecture was suggested. For accuracy, DR, and FAR, respectively, the experimental findings revealed 80%, 81%, and 15.1%. According to the authors in [46], we used a feature subset based on correlation, information gain, symmetrical uncertainty, and NSL-KDD datasets utilizing GAR-Forest, which stands for greedy randomized adaptive search strategies. Information gain reached an accuracy of 78.9035% with 10 features for multi-class, while the results indicated an accuracy of 85.0559% with 32 features for binary class. The authors in [47] suggested using NSL KDD and recognizing attacks on wireless sensor networks. To this end, \"anomaly detection in hybrid wireless sensor networks and machine learning techniques for energy efficiency\" were used. According to the experimental findings, precision, re-call, and F1-Score were 94.00%, 98.00%, and 96.00%, respectively, while accuracy was 95%. They introduced the CSE-IDS, which uses cost-sensitive DL and ensemble approaches to address an unbalanced class of intrusion detection systems. Phase 1 separates and disperses routine or suspicious network attacks using a deep neural network (DNN). There are three phases that must be discussed. Extreme gradient boosting categorizes phase 2 primary attacks, whereas phase 3 adopts RF to classify minor attacks. They used the datasets NSL_KDD, CIDDS 001, and CIC_IDS2017 to assess the performance of the system; the accuracy for NSL, CIDDS-001, and CIC_IDS2017, respectively, was 99%, 96%, and 92%, although the complexity time measurement took several hours [48]. The researchers used these datasets to evaluate the system's performance. To extract features, the authors used a stacked auto-encoding network [49]. They then suggested a random forest, SVM, and another classification technique. Using a hybrid DNN-KNN methodology, the authors in [50] provided a binary classification. There were several ML and DL hybrid algorithms. In [51], authors employed ensemble learning approaches and feature selection (i.e., correlation feature selection-forest attribute). The study's experimental findings exclusively utilized the CIC IDS2017 dataset. Additionally, when employing 30 feature-selected tests, the testing accuracy was 87%. Using just two datasets--NSL-KDD and UNSW BN2015--the authors of [52] introduced correlation feature selection methods. These approaches allow users to choose the optimal feature. Furthermore, they only selected those datasets 30 features.