## 3 Proposed Method
### The Process Outline of the New Approach  
The suggested technique is based on five steps: \"collecting\" logs, \"preprocessing\" the logs, \"dimensionality reduction\", \"selecting\" a model, and \"evaluating\". The steps involved in the suggested technique are shown in Figure 7. A quick rundown of what should happen at each stage is provided below. (a) Data Collection/Log Collection: To serve as a standard for anomaly detection methods, the BGL (Blue Green/L supercomputer system) dataset was acquired. This \begin{table}
\begin{tabular}{l l l l l} \hline
**Ref** & **Algorithms/Methodology** & **Data Set (s)** & **Accuracy** & **Limitations** \\ \hline \multirow{4}{*}{[28]} & \(\bullet\) & The unsupervised & \multirow{4}{*}{92\%} & \multirow{4}{*}{Variations in datasets might cause issues with precision and processing efficiency.} \\  & \(\bullet\) & Log clustering & & & \\  & \(\bullet\) & PCA & & & \\  & \(\bullet\) & Invariant mining & & & \\ \hline \multirow{4}{*}{[29]} & \(\bullet\) & SVM linear & \multirow{4}{*}{Not available} & \multirow{4}{*}{F-measure 85\%} & \multirow{4}{*}{Does not support firewall attacks} \\  & \(\bullet\) & SVM bRE & & & \\ \hline \multirow{4}{*}{[30]} & \(\bullet\) & SD (subgroup & \multirow{4}{*}{Not available} & \multirow{4}{*}{Threshold} & \multirow{4}{*}{The subgroup finding approach may be replaced with other methods that perform more effectively.} \\  & discovery) & & & & \\ \hline \multirow{4}{*}{[31]} & \(\bullet\) & RNN & IE & & \\  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 7: Workflow of the proposed method. dataset, labeled for unsupervised algorithms, has 4,757,963 log messages and 3,484,64 anomalies that may be used in the training of these algorithms. This dataset was generated by data mining specialists may now be used to test the performance of algorithms on various tasks [32]. (b) Preprocessing: The data preparation process is essential in machine learning algorithms. The methods utilized to gather data do not provide a feature that is both accurate and useful for feeding into machine learning programs. However, if no preprocessing is performed beforehand, the model given the raw data may provide misleading results. For this reason, it is crucial to define data in the appropriate format before training algorithms, so that algorithms may properly operate on the data without causing undesirable repercussions. The data collection process includes several steps, including instance selection, normalization, dimension reduction, and feature selection. However, investing in the time-consuming procedures of data preparation and data filtering is crucial to obtaining reliable findings. (c) Dimensionality Reduction: Datasets that include a huge number of characteristics hinder the comprehension or exploration of the relationships among them. The exploratory data analysis (EDA) phase might drag on and perhaps affect the efficacy of the ML models being used. As a machine learning technique for sparse representation and visualization, we used the t-distributed stochastic neighbor embedding (t-SNE) method developed by [PERSON] and [PERSON] [33]. (d) Model selection: We next used the machine learning methods K-means, one-class SVM, and isolation forest after the data were preprocessed. The first step in choosing a model is to analyze the dataset in depth to discover the types of features present. Machine learning algorithm selection is the process of selecting the best machine learning algorithms from a set of candidates. K-means, a one-class support vector machine, and isolation forest were the three methods we settled on. (e) Performance Evaluation: Machine learning relies heavily on performance evaluations. The goal of a performance evaluation is to find out how well a model or strategy works with extended data. To evaluate the efficacy of the model, we used numerous metrics, including F1-score, sensitivity, and specificity.