Cybercrime Analysis using Machine Learning Algorithms: Decision Tree, Naïve Bayes, Random Forest and K-Nearest Neighbor
Sangeetha V Sebastian, Meera Rose Mathew · Zenodo (CERN European Organization for Nuclear Research) · 2022
Abstract—Cybercrime is defined as a crime committed through the use of a computer or network. Cybercrime is growing day by day; it is one of the main crimes executed via internet and technology. Cyberattack can lead to loss of a lot of sensitive data, including personal information, password and credit card numbers. Machine Learning has arisen as a major technique in the field of cybercrime protection. Through design distinguishing proof, ongoing cybercrime planning and broad entrance testing, AI proactively gets rid of digital dangers and fortifies security foundation. In this study, we use four machine learning algorithms: Decision Tree, Random Forest, Naïve Bayes and K-Nearest Neighbor to analyze cybercrime. Among the used cybercrimes Random Forest and Decision tree proved to be the most accurate.