Intelligent System for Detecting Intrusion with Feature Bagging

Abstract

Cyber-security has received considerable attention as a result of individuals and businesses’ enormous impact on the Internet and their concern about the security and privacy of their online activities. Due to this, predicting cyberattacks with machine learning has become crucial as the number of attacks has risen dramatically as a result of attackers’ stealth and sophistication. To maintain situational awareness and achieve defense in depth, collecting cyber threat intelligence requires the use of machine learning for threat prediction. With the increasing use of technology, intrusion detection has become a flourishing field of study. It monitors and alerts users to their typical (or) anomalous behavior. IDS is a nonlinear and challenging task that entails analyzing network traffic data. The purpose of this article is to examine the potential of employing machine learning approaches to forecast malware attacks. The objective is to foresee the types of network attacks that may occur. To demonstrate our work’s usefulness, we employed a random forest approach to learn the assessment dataset. This is where the random forest comes in handy.