Enhancing the Efficiency of Gaussian Naïve Bayes Machine Learning Classifier in the Detection of DDOS in Cloud Computing

Abstract

Distributed Denial of services is one of the most dangerously planned attacks in cloud computing, resulting in huge losses of data and money for both the cloud services providers and the users of these services. Many efforts have been performed to help protect the cloud from these attacks using machine learning techniques. This study focuses on enhancing the efficiency of the Gaussian Naïve Bayes classifier, considered one of the cheapest and fastest classifiers. Still, it has some problems resulting from its equation's statistical nature. The nature of this classifier is based on multiplication, resulting in inaccurate classification due to the zero-frequency issue and the fact that it assumes that features are independent. This research proposed a framework handling the selection of a set of highly independent features following an iterative feature selection approach using the Pearson Correlation Coefficient, Mutual Information, and Chi-squared and then selecting other subsets of features from these sets to reach a set of highly independent features. After that, we used a specific algorithm handling the data pre-processing to handle the zero-frequency problem where we used the Mode to replace the missing values, and if the mode was zero, it used the mean instead. Still, if the record's label is zero, we get the value of the previous record with zero labels. After that, we handled the data imbalances using SMOTE. These enhancements increased both accuracy for the mutual information model by 2% and the average overall accuracy and precision by 1.5%.

Evaluating the Performance of Voting Classifier in Multiclass Classification of Dry Bean Varieties

“…The study adopts an experimental research design, focusing on the application of machine learning algorithms for the classification of dry bean varieties. The core of the research involves the development and evaluation of a voting classifier that integrates the predictions of Decision Tree [9], Logistic Regression [10], and Gaussian Naive Bayes [11] classifiers. The effectiveness of this ensemble method is assessed through cross-validation and performance metrics such as accuracy, precision, recall, and F-measure [12].…”

Analyzing the Temporal Interplay and Contribution of Socioeconomic, CO₂ Related Industry, and Education to the Year-on-Year Change in CO₂ Emissions: An In-Depth Analysis Using Machine Learning Approach

“…For the regressions approach, the performance of following regressors was compared: Linear Regression [10], Ridge Regression [11], Bagging Regressor [12], Random Forest Regressor [13], Gradient Boosting Regressor [14], XGBoost Regressor [15], AdaBoost Regressor [16] and KNeighbors Regressor [17] . Concerning classification, the performance of the following classifiers was compared: Logistic Regression (LogReg) [18], Decision Tree (DT) [19], Random Forest Classifier (RF) [20], XGBoost classifier (XGB) [21], Multi-Layer Perceptron classifier (MLP) [13], Bagging (BC) [22], AdaBoost (ABC) [23], Gradient Boosting (GB) [24], Support Vector (SVC) [25], Gaussian Naïve Bayes (GNB) [26].…”

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

“…The study [50] aimed to enhance the efficacy of the Gaussian Naïve Bayes classifier to detect DDOS attacks in cloud computing. It discussed the prevalence of DDOS and DOS attacks against cloud services, as well as the difficulty in detecting these attacks due to their distributed character and potential for catastrophic consequences.…”