Trends in artificial intelligence techniques, in the detection of computer crimes: Systematic Review of the Literature (SLR)
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Abstract
In this article, a Systematic Literature Review (SLR) is presented that focuses on the applications of Artificial Intelligence (AI) techniques for detecting cybercrimes. The first part of the review is dedicated to selecting the sources of information to be used, while the next section provides a detailed description of the research that has employed these AI techniques. During this research process, it was evident that the majority of the studies have used a variety of AI algorithms. Among the most frequent ones are SVM, Decision Tree, Logistic Regression, Naive Bayes, KNN, and Random Forest, which have demonstrated their effectiveness in multiple areas of cybersecurity, including intrusion detection, Denial of Service (DoS) attacks, phishing, and malware. In this context, it has been observed that XGBoost, Random Forest, and Logistic Regression stand out for their remarkable balance between precision and accuracy metrics. The findings emphasize the need to adapt the choice of algorithm according to the dataset and specific context, highlighting the importance of conducting meticulous tests and definitions. Finally, the results obtained from this review provide an enlightening guide that can guide decisions, offering readers a glimpse into the most promising techniques in areas that deserve greater attention, as well as exploration for future research.
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Joseph Alberto Delgado-Indacochea, Universidad Técnica de Manabí - Ecuador
https://orcid.org/0009-0009-2343-2643
Student of Information Systems Engineering, Faculty of Computer Science, Technical University of Manabí.
Roberth Abel Alcívar-Cevallos, Universidad Técnica de Manabí - Ecuador
https://orcid.org/0000-0001-6282-8493
Doctor in Engineering Sciences with a mention in computer science, Professor in the Information Technology Department, Faculty of Computer Sciences, Technical University of Manabí.
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