Early Detection And Mitigation Methods For Sql Injection Attacks Using Adaptive Free Algorithm

Abstract

SQLIA (Structured Query Language Injection Attack) is a relatively common web security flaw. The attacker inserts harmful Structured Query Language (SQL) script into an online form's input in order to control answers to information or make unauthorized modifications to it. A successfully hostile SQL injection has substantial economic, reputational, security, and governmental consequences for the afflicted firm. Numerous studies have been conducted on the detecting and preventing of SQL injection attacks. Nevertheless, significant single tools for the both detection and prevention of SQL injection threats still are lacking. We've presented a method for detecting and mitigating SQL injection attacks to use a single tool, which helps software engineers to spot SQL injection security flaws throughout the assessment process. In this work, deep learning approach is proposed for detection of SQL Injection Attacks using Convolutional Neural Networks (CNNs). Then to prevent the SQL Injection Attacks, authentication by means of Asymmetric Encryption is proposed which is known as Optimum Prime Number Selection based Elliptic Curve Cryptography (OPNS-ECC). In this case, genetic algorithm is proposed for the optimum number selection. The proposed methodology is based on parametric searches and authentication of user input. Our findings reveal that the tool is fully accurate and effective when it comes to identifying and mitigating SQL issues.