Abstract

Cyber-incidents are a mixture of discrete instances with new illegal acts. Cybercrime incidents occur as separate criminal offenses, and, according to national crime statistics and surveys, the instances are increasing. The existing system classifies cybercrimes and cyberincidents with less accuracy. These overlapping results and the lack of a unique algorithm for classification are the main drawbacks of the existing model. Thus, to solve these problems, the paper gives an exclusive way of classifying various crimes depending on physical factors such as time and date. It gives users a solution to carry out an efficient classification outcome using a cybercrime classifier with support vector machines (SVM). It uses the grouping of the dataset by either decision trees or random forests to build up a model to prepare over a preparation set in order to get the most exact outcomes. It is a modest and productive approach to group cybercrimes, with the goal that the affected can identify the kind of occurrence and follow-up accordingly. Additionally, it examines the information and creates various charts for the correct portrayal of the information. The above model, designed to categorize convicted criminals into low, medium, and high risk of turning into recidivists, helps curb the increasing crime rates in society, thus ensuring the welfare and well-being of its citizens. The extensive simulation results show that the proposed method is an outstanding classifier compared to state-of-the-art approaches.

Description