Machine Learning-Based Detection and Mitigation of XML SQL Injection Attacks

XML and SQL injection attacks are occurring very frequently nowadays as developers lack major security measures and awareness for the purpose of securing web applications and documents. Many factors are responsible for these types of attacks and vulnerabilities are the main reasons behind all these types of attacks like urbanisation, environmental degradation and some of the market conditions are responsible for these types of vulnerabilities in the market and lead to such attacks. These may also allow the attackers to gain advantage to overlook the front end of the application by taking advantage of all the vulnerabilities of input fields like usernames, passwords, and important credentials. This research paper covers the detailed study of the occurrence of XML injection attacks on front end development applications, also the detection and mitigation policies employed in the literature. The machine learning based model is presented in this case study for detecting XML SQL injection attacks and the mitigation policies are also discussed. The classification machine learning employed in the research paper are also compared using metrics based on accuracy, precision, Specificity and F-1 Score.