The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of t...The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of technolo-gical suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights.In this paper,the authors pro-posed the HDTbNB(Hybrid Decision Tree-based Naïve Bayes)algorithm tofind the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS(Intrusion Detection System)and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes,Decision Tree,K-Nearest Neighbors and Logistic Regression over KDD 99 data-set.The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy,macro average,and weighted average.Thefindings were concluded as a percentage increase in accuracy,precision,sensitiv-ity,specificity,and a decrease in misclassification as 9.3%,6.4%,12.5%,5.2%and 81%.展开更多
文摘The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of technolo-gical suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights.In this paper,the authors pro-posed the HDTbNB(Hybrid Decision Tree-based Naïve Bayes)algorithm tofind the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS(Intrusion Detection System)and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes,Decision Tree,K-Nearest Neighbors and Logistic Regression over KDD 99 data-set.The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy,macro average,and weighted average.Thefindings were concluded as a percentage increase in accuracy,precision,sensitiv-ity,specificity,and a decrease in misclassification as 9.3%,6.4%,12.5%,5.2%and 81%.
