# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 2 Related Work
By utilizing an ensemble learning (EL) algorithm-based anomaly detection in communication networks (ADCNs), the authors of [53] suggested a unique anomaly detection in communication networks. In each of the three datasets (NSL_KDD, UNSW_NB2015, and CIC_IDS2017) individually, it minimizes dimensionality and finds the best subset feature. Their results were obtained by applying 30, 35, and 40 features of the system tothe three datasets, which resulted in accuracy values of 99.6% for NSL_KDD, 99.1% for UNSW_NB2015, and 99.4% for CIC_IDS2017. Since the work in [53] was the primary study, we applied the same research methods and reimplemented its algorithms for the purpose of benchmarking with a variety of dataset pre-processing and feature combinations. In addition to that, we utilized the more recent dataset known as CICDDOS2019 and concentrated our attention on data anomalies in 6G cellular networks. For 5G networks, the authors of [54] offered a special feature extraction and detection system where the deep belief network (DBN) and bidirectional long-short-term memory (Bi-LSTM) were utilized as part of a hybrid classifier (HC). The weights of both Bi-LSTM and DBN are optimized using a novel deer hunting updated sun flower optimization (DHSFO) model, which combines the concepts of sun flower optimization (SFO) and deer hunting optimization (DHO) methods, to transform the detecting stage properly and precisely. A unique architecture was put forth by the developers of [55] with the goal of proactively detecting such traffic anomalies or forecasting impending 5G network traffic anomalies before they happen. Clustering and decision tree-based learning are applied to automatically identify anomalies in 5G network traffic. For the pro-active identification of impending network traffic behaviors, a time series model--more specifically, a Bidirectional long short-term memory (BiLSTM) autoencoder--is used. According to evaluation results, the proposed framework is effective and viable and has the potential to be more successful than a state-of-the-art solution, with a prediction accuracy of up to 90.02%. Using a deep-learning methodology, the authors of [56] proposed a new technique for anomaly identification that is based on the autoencoder principle and long-short-term memory (LSTM) recurrent neural networks. Real sensor data was used in a number of simulations, and the accuracy of the discovered abnormalities was assessed. The findings indicate that the RNNs have an accuracy of 87% in detecting anomalies over both short and extended periods of time. SDWSNs and RNNs can therefore be utilized as a first step in effectively detecting different kinds of anomalies. Many researchers have examined distributed machine learning techniques [57; 58] and found that they significantly reduce the complexity of high dimensional data. These researchers have demonstrated the advantages of utilizing ML algorithms to handle enormous amounts of data for the IDS pre-processing step. However, DL algorithms could access hidden features in the step that classified anomalies from many targets to find unidentified attacks. They achieve the highest rates of false alarm, false negative, and detection, and they greatly outperform state-of-the-art technologies while consuming the least time when applied to many datasets. To address computing cost and privacy preservation issues, the authors of [59] introduced a synonym-based multi-keyword ranked search over encrypted cloud data. To allocate an index vector for each document, the suggested mechanism builds an m-Way search tree. It then uses a depth-first search to compute the top score ranking, improving search efficiency. The examination's findings demonstrate that the presumed index vectors might be separated into sub-vectors before being stored in the index tree to improve computing efficiency and security.