Evaluación de algoritmos de mineria de datos para detección y predicción de ataques de Inyección sql en big Data: evaluación del algoritmo naive bayes para la detección y predicción de ataques de inyección sql en big data.

Abstract

In the current era, SQL Injection Attacks (SQLIA) are ranked third in the OWASP (Open Web Application Security Project) TOP 10 list for the year 2021 since it is relatively easy for a computer attacker to execute this type of attack. However, when a computer attacker executes, this type of attack can have a great impact on information systems. Furthermore, knowing that combating this type of attack is complex for organizations, this component is responsible for systematically investigating the literature on the use of algorithms and data mining techniques for detection and prevention of SQLIA. Then, taking this research as a reference, a data mining algorithm is selected, which in this case is Naive Bayes, and the evaluation of this algorithm is carried out to determine how effective it is at detecting SQL sentences that may be malicious. Next, a prototype web system is developed to help analyze log records using the Naive Bayes algorithm to detect and predict SQLIA. The main objective of this component is to improve the security of the organization’s information systems, in which the developed system will be implemented.