Predictive Analysis of Cyber Intrusion Detection through Machine Learning Models

Abstract

The increasing prevalence and complexity of cyber attacks pose significant threats to global security, leading to considerable financial and operational disruptions. Conventional models of crime prediction often lack the flexibility necessary to predict the changing trends of cyber attacks accurately. This article represents a framework of machine learning for analysis and prediction of cyberattacks by means of forensic data in the real world, such as the specifics of attack, demography of the perpetrators, damage to property, and attackers. Two prediction models have been created and evaluated using eight machine learning algorithms to determine the most effective strategies to identify the attack techniques and assign responsibility. The linear model of the SVM indicated greater accuracy in categorizing forms of cyberattacks, while logistic regression showed significant efficiency in detecting enemy actors. The investigation indicated that people with increased education and economic levels have a reduced probability of victimization. The proposed technique integrates predictive analytics into examination processes for computer crime and provides special information on improving detection, assignment, and preventive abilities. These results have considerable potential for implementation into units in computer crime and therefore increase the efficiency of cybersecurity tactics.