Predictive modeling and categorization of cyber threats using data mining techniques

Mohammed Javed Hussain; Sarwesh P

Author(s)

Mohammed Javed Hussain Department of School of Computer Science and Engineering Vellore Institute of Technology, Vellore, Tamil Nadu, India

Sarwesh P Department of Software Systems Vellore Institute of Technology, Vellore, Tamil Nadu, India t

Abstract

Numerous IoT devices and the quick advancement of technology make cyberspace unsafe, which ultimately results in Significant Cyber Incidents (SCI). One method for preventing SCI in online systems is cyber security. Cyber security relies heavily on Data Mining and Machine Learning (DM-ML) for SCI detection, prevention, and prediction. Pre-pandemic and post-pandemic SCI are the two subgroups of the dataset (SCI according to the Center for Strategic and International Studies (CSIS) study). Well-known machine learning classifiers including Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) are utilized for classification, while data mining (DM) techniques are employed for feature extraction. One consolidated dataset is maintained using a centralized classifier technique by using contributions from the world's six continents. This research concludes with improved accuracy and the prediction of which form of SCI can occur in which portion of the world by comparing the results of the pre-pandemic and post-pandemic datasets. Asia is anticipated to be the continent most impacted by SCI, and it is determined that SVM and RF are far superior classifiers than others.

Publication Details

Page(s) 283-290

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 5, Year: 2024

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