A systematic literature review on the use of artificial intelligence for cybercrime rate forecasting

Abstract

Cybercrime has a significant impact on the quality of life and economy of individuals, businesses and countries, and the speed of the increase has made it a pressing issue in today's digital age. This systematic review aims to identify the artificial intelligence models recently developed to forecast the rate of cybercrime and to help authorities and police forces define strategies in the fight against cybercrime. The PRISMA methodology was used with 229 articles retrieved from Scopus, IEEE and Web of Science, of which 30 met the eligibility criteria. The results showed that the traditional machine learning methods random forest, support vector machine (SVM) and logistic regression (LR) excel in their use to forecast cybercrimes by achieving more accurate results among the different methods tested. It was concluded that machine learning methods are, so far, effective in forecasting the rate of cybercrime, with accuracy ratios of up to 99.9%. However, the potential for future research lies in creating new forecasting models such as autoregressive integrated moving average long short term memory (ARIMA-LSTM) proposed in this study to improve the performance and accuracy of cybercrime forecasting.