Revisión sistemática de la literatura relacionada con ciberseguridad apoyada con analisis de Big Data para actividades de red Team

Abstract

This research reviewed the existing literature during the period 2017 to 2021 on Cybersecurity supported by Big Data Analysis, evidencing the use of techniques to detect cyberattacks. The study methodologies used were Systematic Mapping and Systematic Literature Review in scientific papers, in order to suggest some Red Team activities to check the Cybersecurity. In each academic article were identified Cybersecurity anomalies, Big Data Analysis techniques, classification algorithms, metrics and performance results of the algorithms. During the investigation were identified 13 Cybersecurity anomalies, 10 Big Data Analysis techniques, 16 classification algorithms and 15 performance metrics. The Cybersecurity anomalies with the highest incidence in the studies were Phishing with 41.94%, Network Attacks with 12.9%, Malware with 9.68% and Social Engineering with 6.45% each one. Finally, to check Cybersecurity with Big Data Analysis, its recommend a Red Team analysis on the highest incidence anomalies using open source weapons, like: SET, METASPLOIT, DLL and OWASP ZAP.