Fuzzy neural networks to create an expert system for detecting attacks by SQL Injection

TL;DR

This paper presents a fuzzy neural network-based expert system for detecting SQL Injection attacks, demonstrating its effectiveness in classifying cyber-attacks with accuracy comparable to existing models.

Contribution

It introduces a hybrid fuzzy neural network model for cyber-attack detection, specifically targeting SQL Injection, enhancing automated security measures.

Findings

Achieved classification accuracy within standard deviation of state-of-the-art models.

Validated system effectiveness using real SQL Injection attack datasets.

Supports automated attack detection to improve cybersecurity defenses.

Abstract

Its constant technological evolution characterizes the contemporary world, and every day the processes, once manual, become computerized. Data are stored in the cyberspace, and as a consequence, one must increase the concern with the security of this environment. Cyber-attacks are represented by a growing worldwide scale and are characterized as one of the significant challenges of the century. This article aims to propose a computational system based on intelligent hybrid models, which through fuzzy rules allows the construction of expert systems in cybernetic data attacks, focusing on the SQL Injection attack. The tests were performed with real bases of SQL Injection attacks on government computers, using fuzzy neural networks. According to the results obtained, the feasibility of constructing a system based on fuzzy rules, with the classification accuracy of cybernetic invasions within the margin of the standard deviation (compared to the state-of-the-art model in solving this type of problem) is real. The model helps countries prepare to protect their data networks and information systems, as well as create opportunities for expert systems to automate the identification of attacks in cyberspace.