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SQL Injection Attack Detection and Prediction using Machine Learning Predictive Analytics

Suhas GK, Sinchana K M, Yashaswini KN, Nishma M N, Hameeda Banu

Abstract


The world depends heavily on web apps these days. Consequently, it is crucial to secure these apps. In the majority of apps, data is kept in the backend databases. The Structured Query Language Injection Attack (SQLIA) is one of the flaws. Today, there are numerous apps available to harvest HTTP cookies from sessions. These attacks can be thwarted using a wide variety of methods.The proposed work examines the shortcomings of some of these defences against these assaults and employs a powerful hashing method to counteract it. To defend against the aforementioned attacks, the Support Vector Machine (SVM) algorithm and the machine learning concept were suggested. It is used to detect and prevent SQL injection. Before building the model, this technique trains the SVM algorithm with all potentially dangerous expressions.


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