![]() ML approach can be used to mitigate a very hard blind SQL injection attack. Machine Learning Approach requires a lot of data for efficient model training with capability for using several attack patterns. Machine Learning (ML) approach has been found to be profound for SQLIA mitigation, which is implemented through defensive coding approach. There are several solutions and approaches for identification and prevention of SQLIA, such as Cryptography, Extensible Markup Language (XML), Pattern Matching, Parsing and Machine Learning. Structured Query Language Injection Attack (SQLIA) is one of the most prevalent cyber attacks against web-based application vulnerabilities that are manipulated through injection techniques to gain access to restricted data, bypass authentication mechanisms, and execute unauthorized data manipulation language. If a system is compromised, organizations need to improve the ability to minimize their damage.This paper approaching the difficult problem of mitigation of security risk vulnerabilities with which most organizations are confronted today.The purpose of this paper is to inform organizations of this rapidly growing problem and provide best-practice defense tactics. In order to minimize the opportunity for sensitive information from " leaking out " of an organization, it is crucial to increase user awareness regarding information security issues. ![]() Risk factors are calculated for each of the discovered vulnerability in order to prioritize remediation activities accordingly.This paper discussed the remediation plans for mitigation of common vulnerabilities encountered in organization " s computing environment. These risks are quantified accordingto their likelihood of occurrence and the potential damage if they occur. This paper investigated the security risks that could adversely affect organization " s critical operations and assets.
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