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

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%.