# Detection of SQL Injection Attack Using Machine Learning Techniques: A Systematic Literature Review
## 4 Results
### Conducting the Review
[27] used the AdaBoost algorithm to detect SQL injection attacks. In this study, the data were converted into stumps, which were classified as weak stumps providing less weight to the output or strong stumps providing the highest weight in the overall output. The experimental result showed that the proposed algorithm accurately and effectively detected injection attacks. [PERSON] al. [28] proposed a method for classifying dynamic SQL queries as either attacks or normal based on a web profile prepared during the training phase. Naive Bayes, SVM,and parse tree approaches were used for the classification process. The overall detection rate using the two datasets was 91% and 90%, respectively. [PERSON] [29] designed a method to detect malicious SQL queries. Decision tree algorithms were used for the classification processes to detect different levels of SQL injection. The proposed model maintained an accuracy more than 98% in detecting SQL injection attacks and an accuracy of 92% in classifying the level of attack as simple, unified, or lateral. [PERSON] et al. [30] presented a simple method for SQL injection attack detection based on an artificial neural network. First, a large amount of SQL injection data were analyzed to extract the relevant features. Then, a variety of neural network models, such as MLP and LSTM, were trained. The experimental results showed that the detection rate of MLP was better than that of LSTM. [PERSON] al. [31] automatized the process of exploiting SQL injection attacks through reinforcement learning agents. In this study, the problem was modelled as a Markov decision process. The experimental results show that reinforcement learning agents can be used in the future to perform security assessment and penetration testing. [PERSON] et al. [32] presented a detection method by modeling SQL queries as a graph of tokens and utilized the centrality measure of tokens to train single and multiple SVM classifiers. The system was tested using directed and undirected graphs with different SVM classifiers. The experimental results demonstrated that the proposed technique is able to effectively identify malicious SQL queries. [PERSON] et al. [33] presented a model of a two-class support vector machine (TCSVM) to predict binary labelled outcomes concerning whether an SQL injection attack was positive or negative in a web request. This model intercepted web requests at the proxy level and applied ML predictive analytics to predict SQL injection attacks. [PERSON] et al. [34] presented a novel approach for classifying SQL queries. A gap-weighted string subsequence kernel algorithm was used to compute the similarity metric between the query strings. Then, the support vector machine was trained on the similarity metrics to determine whether the query strings was normal or malicious. The proposed approach was evaluated using a number of datasets and achieved 92.48% accuracy. [PERSON] et al. [35] presented a new approach to the construction of a dataset with a NoSQL query database. Six classification algorithms were trained and evaluated to identify SQL injection attacks, which included: decision tree, SVM, random forest, k-NN, neural network, and multilayer perceptron. The experimental results showed that the last two algorithms obtained an accuracy of 97.6%. [PERSON] et al. [36] trained a progressive neural network model with a naive Bayesian classifier to successfully detect SQL injection attacks. Progressive neural networks were trained using parameters such as error-based, time-based, SQL query and, union-based SQL injection attacks. The proposed method achieved an accuracy of 97.897%. [PERSON] et al. [37] proposed a hybrid approach using tree-vector kernels in SVM to learn SQL statements. The authors used both the parse tree structure of SQL queries and the query value similarity characteristic to distinguish between malicious and benign queries. The results confirmed the benefit of incorporation to efficiently and accurately identify abnormal queries. [PERSON] et al.