Online ISSN: 2515-8260

Author : Balamurugan, K.

Applied Machine Learning Predictive Analytics to SQL Injection Attack Detection and Prevention

*T.P. Latchoumi; Manoj Sahit Reddy; K. Balamurugan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3543-3553

These days the world is very much dependent on web applications. Hence providing security to
these applications is of great importance. Information is maintained in the backend databases in
the majority of applications. Among the vulnerabilities is the Structured Query Language
Injection Attack (SQLIA). There are several applications to retrieve session/HTTP cookies
nowadays. There is quite a range of techniques used to stop these attacks.The proposed work
discusses the flaws in a few of these techniques that handle these attacks and implement an
efficient hashing technique to prevent this technique. To overcome the above-mentioned attacks,
the machine learning concept with the Support Vector Machine (SVM) algorithm was
introduced. It is used to detect and prevent SQL injection. In this technique, the SVM algorithm
will be trained with all possible malicious expressions and then generate the model. Whenever a
user gives any new query then SVM will be applied to that model to predict whether a given
query contains any malicious expressions or not. If the user invents the new technique then also
SVM can detect that malicious expression by matching with a minimum number of syntax.