Anomaly Prediction in Cybersecurity: A Machine Learning Model from the Perspective of Data Engineering and Fingerprinting
Year: 2024
Type: conference-paper
Abstract: This project utilizes artificial intelligence (AI) and machine learning through the development of a mathematical-predictive model to reliably detect cyber anomalies. Using the BETH dataset and the CRISP-DM methodology, this research has addressed the problem of combining kernel and network traffic data, achieving a 37.27% increase in the detection of malicious activities compared to the initial data. Additionally, an innovative dataset was formulated, in which the complete trace of a new botnet attack pattern was discovered, previously unknown to BETH, involving the entire monitored network in illicit cryptocurrency mining. Finally, several models were successfully built and trained using Random Forest and Decision Trees algorithms, with accuracies of 100% and 99%, respectively.
Authors: Víctor Martínez Martínez, Verónica Bravo Alonso
Institutions: Universidade da Coruña, Universidad Isabel I
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4407548934