SQL Injection Attack prevention based on decision tree classification

Abstract

In real world as dependence on World Wide Web applications increasing day by day they transformed vulnerable to security attacks. Out of all the different attacks the SQL Injection Attacks are the most common. In this paper we propose SQL injection vulnerability prevention by decision tree classification technique. The proposed model make use famous decision tree classification model to prevent the SQL injection attacks. The proposed model will filter the sent HTTP request by using a decision tree classification based attack signatures. We test our proposed model on synthetic data which given satisfactory results.

SQL Injection Detection for Web Applications Based on Elastic-Pooling CNN

“…For comparison, traditional machine learning methods are used to classify and compare the accuracy and F1 values, including SVM [2], [3], Naive Bayes [4], [6], Decision Tree [4], [5], and Random Forest [23], all of the training sets for these traditional machine learning methods is randomly selected from the training sets of EP-CNN. After many tests, we use 10000 query strings to train SVM and Naive Bayes model, and 100000 query strings to train Random Forest and Decision Tree model.…”

“…After vectorizing the extracted features, the traditional machine learning method is used for classification and recognition. Naive Bayes [4], [6], Decision Tree [4], [5], and Support Vector Machine (SVM) [2], [3] are commonly used. There are many detection methods that do not use machine learning and that include classification technology based on user behavior [12], [13] and expectation criteria [14].…”

“…Existing methods for identifying SQL injection attacks include regular matching [1], Support Vector Machine (SVM) technology [2], [3], Decision Tree [4], [5], Naive Bayes [4], [6], etc. Among them, regular matching is widely…”

SQL Injection Detection Using Machine Learning Techniques and Multiple Data Sources

“…al [3] the authors are using Naive Bayes and Bayes Net techniques which are creating probability models to classify traffic. In Hanmanthu et al [13] the authors are using a decision tree technique, and are also evaluating their results in terms of performance characteristics. Another popular technique in current IDS research is the use of Neural Networks, discussed below.…”

“…A decision tree algorithm iterates through the features of the dataset and chooses the feature with the highest information gain and the best coverage of the dataset, in other words it prefers predictive decisions that contain larger numbers of records. In Hanmanthu et al [13] the authors are using a custom decision tree technique, and are also evaluating their results in terms of performance characteristics. Their accuracy on SQL injection data is in the 86-87% range.…”