Evaluación de Algoritmos de Mineria de Datos para Detección y Predicción de Ataques de Inyección SQL en Big Data.

Abstract

Currently, SQL Injection Attack (SQL Injection Attack or SQLIAS) is in the third position on the OWASP (Open Application Security Project) list .This attack is very easy to exploit and compromises the security of a system. However, some tools and technologies mitigate the threat posed by these attacks. In this component, a Systematic Review of the Literature related to the use of data mining algorithms to detect and prevent SQL Injection Attacks was carried out. From the results of this research, Artificial Neural Networks has been selected as the object of evaluation to determine the degree of effectiveness in detecting text sequences that could result in a SQL Injection Attack. Finally, a web application is implemented to process and monitor the information contained in logs of data entry records using the model previously created for deteting and preventing of SQL Injection Attacks. This component aims to increase the confidence and security of the organization’s systems where it is implemented.