Online ISSN: 2515-8260

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

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1,*T.P.Latchoumi, 2Manoj Sahit Reddy, 3K.Balamurugan


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.

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