SQL Injection Attack Detection Using Machine Learning Algorithm

One of the most widely recognised cyber-assaults against web-based application weaknesses is the structured query language injection attack (SQLIA), which is utilised to execute unlawful information control language, evade confirmation strategies, and access confined information. Some published systematic reviews were considered in this area. Older and more current papers in the field are often included in more recent systematic reviews. As a result, all of the publications we looked at were recent. I used data from 2012 to 2020 for the present analysis. There are a few techniques and systems for identifying and forestalling SQLIA, including encryption, XML, design coordinating, parsing, and machine learning. Guarded coding is utilised to apply Machine Learning (ML) procedure, which has been shown to be significant for SQLIA alleviation. The machine learning approach needs a ton of information to prepare models really and support a few attack types. An extremely difficult visually impaired SQL injection attack might be relieved utilizing ML procedures. An exploratory examination of Logistic Regression (LRN), Stochastic Gradient Descent (SDG), Sequential Minimal Optimization (SMO), Bayes Network (BNK), Instance-Based Learner (IBK), Multilayer Perceptron (MLP), Naive Bayes (NBS), and J48 was completed in the Waikato Climate for Information Investigation. The presentation of the regulated learning grouping calculations was surveyed utilizing Wait (70%) and 10-crease Cross Validation appraisal methods to decide the best calculation. According to the findings of the Cross Validation approach, SMO, IBK, and J48 had accuracy values of 98.7785%, 98.4285%, and 98.2985%, respectively, while the Hold-Out technique revealed accuracy values of 98.7956%, 98.1526%, and 100 for SMO, IBK, and J48. In contrast, IBK and J48 needed 10.15 seconds, 0.06 seconds, and 14.12 seconds, respectively, to create their models using the Cross Validation approach SMO, whereas they needed 9.71 seconds, 0.16 seconds, and 14.28 seconds, respectively, to develop their models using the Hold-Out technique SMO. According to the results, IBK was selected as the classifier for SQLIA detection and prevention since it required the least amount of time to develop a model using the Cross Validation approach and performed better than other candidates in terms of accuracy, sensitivity, and specificity. For the best algorithm selection for predictive analytics, various performance assessment measures are also crucial in addition to accuracy.