Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention  

作　　者：Shyla Vishal Bhatnagar 

机构地区：[1]NSUT East Campus(Formerly Ambedkar Institute of Advanced Communication Technologies and Research),Guru Gobind Singh Indraprastha University,New Delhi,110031,India [2]NSUT East Campus(Formerly Ambedkar Institute of Advanced Communication Technologies and Research),New Delhi,110031,India

出　　处：《Intelligent Automation & Soft Computing》2023年第2期2431-2447,共17页智能自动化与软计算（英文）

摘　　要：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%.

关 键 词：Naïve bayes decision tree k-nearest neighbors logistic regression neighbors classifier 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]