Open Access

ARTICLE

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

Shyla^1,*, Vishal Bhatnagar²

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

* Corresponding Author: Shyla. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 2431-2447. https://doi.org/10.32604/iasc.2023.029126

Received 25 February 2022; Accepted 21 April 2022; Issue published 19 July 2022

Abstract

The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of information flowing over the network. The data will always remain under the threat of technological suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights. In this paper, the authors proposed the HDTbNB (Hybrid Decision Tree-based Naïve Bayes) algorithm to find 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 dataset. The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy, macro average, and weighted average. The findings were concluded as a percentage increase in accuracy, precision, sensitivity, specificity, and a decrease in misclassification as 9.3%, 6.4%, 12.5%, 5.2% and 81%.

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