A Survey on SQL Injection Prevention Methods

Chaki, Shahbaaz Mohammed Hayat; Din, Mazura Mat

doi:10.11113/ijic.v9n1.224

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…In this attack, the injected text is changed to avoid detection by protective coding practices as well as several automated prevention techniques. This attack is used in conjunction with others [92,102]. To intend their attack they use the regular expression [76].…”

Section: Alternative Encoding Querymentioning

confidence: 99%

Detection and prevention of SQLI attacks and developing compressive framework using machine learning and hybrid techniques

Demilie¹,

Deriba²

2022

J Big Data

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A web application is a software system that provides an interface to its users through a web browser on any operating system (OS). Despite their growing popularity, web application security threats have become more diverse, resulting in more severe damage. Malware attacks, particularly SQLI attacks, are common in poorly designed web applications. This vulnerability has been known for more than two decades and is still a source of concern. Accordingly, different techniques have been proposed to counter SQLI attacks. However, the majority of them either fail to cover the entire scope of the problem. The structured query language injection (SQLI) attack is among the most harmful online application attacks and often happens when the attacker(s) alter (modify), remove (delete), read, and copy data from database servers. All facets of security, including confidentiality, data integrity, and data availability, can be impacted by a successful SQLI attack. This paper investigates common SQLI attack forms, mechanisms, and a method of identifying, detecting, and preventing them based on the existence of the SQL query. Here, we have developed a comprehensive framework for detecting and preventing the effectiveness of techniques that address specific issues following the essence of the SQLI attacks by using traditional Navies Bayes (NB), Decision Trees (DT), Support Vectors Machine (SVM), Random Forests (RF), Logistic Regression (LR), and Neural Networks Based on Multilayer Perceptron (MLP), and hybrid approach are used for our study. The machine learning (ML) algorithms were implemented using the Keras library, while the classical methods were implemented using the Tensor Flow-Learn package. For this proposed research work, we gathered 54,306 pieces of data from weblogs, cookies, session usage, and from HTTP (S) request files to train and test our model. The performance evaluation results for training set in metrics such as the hybrid approach (ANN and SVM) perform better accuracies in precision (99.05% and 99.54%), recall (99.65% and 99.61%), f1-score (99.35% and 99.57%), and training set (99.20% and 99.60%) respectively than other ML approaches. However, their training time is too high (i.e., 19.62 and 26.16 s respectively) for NB and RF. Accordingly, the NB technique performs poorly in accuracy, precision, recall, f1-score, training set evaluation metrics, and best in training time. Additionally, the performance evaluation results for test set in metrics such as hybrid approach (ANN and SVM) perform better accuracies in precision (98.87% and 99.20%), recall (99.13% and 99.47%), f1-score (99.00% and 99.33%) and test set (98.70% and 99.40%) respectively than other ML approaches. However, their test time is too high (i.e., 11.76 and 15.33 ms respectively). Accordingly, the NB technique performs poorly in accuracy, precision, recall, f1-score, test set evaluation metrics, and best in training time. Here, among the implemented ML techniques, SVM and ANN are weak learners. The achieved performance evaluation results indicated that the proposed SQLI attack detection and prevention mechanism has been improved over the previously implemented techniques in the theme. Finally, in this paper, we aimed to keep researchers up-to-date, with contributions, and recommendations to the understanding of the intersection between SQLI attacks and prevention in the artificial intelligence (AI) field.

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Section: Alternative Encoding Querymentioning

confidence: 99%

Detection and prevention of SQLI attacks and developing compressive framework using machine learning and hybrid techniques

Demilie¹,

Deriba²

2022

J Big Data

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SQL Injection Detection Using 2D-Convolutional Neural Networks (2D-CNN)

Abdulhamza

Al-janabi

2022

2022 International Conference on Data Science and Intelligent Computing (ICDSIC)

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Using Data Mining Techniques for the Detection of SQL Injection Attacks on Database Systems

Loor

Morocho

Hallo

2023

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In any business organization, database infrastructures are subject to various structured query language (SQL) injection attacks, such as tautologies, alternative coding, stored procedures, use of the union operator, piggyback, among others. This article describes a data mining project developed to mitigate the problem of identifying SQL injection attacks on databases. The project was conducted using an adaptation of the cross-industry standard process for data mining (CRISP-DM) methodology. A total of 12 python libraries was used for cleaning, transformation, and modeling. The anomaly detection model was carried out using clustering by the k – nearest neighbors (kNN) algorithm. The query text was analyzed for the groups with anomalies to identify sentences presenting attack traces. A web interface was implemented to display the daily summary of the attacks found. The information source was obtained from the transactions log of a PostgreSQL database server. Our results allowed the identification of different attacks by injection of SQL code above 80%. The execution time for processing half a million transaction log was approximately 60 minutes using a computer with the following characteristics: Intel® Core i7 processor 7th generation, 12GB RAM and 500GB SSD.

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A Survey on SQL Injection Prevention Methods

Cited by 3 publications

References 28 publications

Detection and prevention of SQLI attacks and developing compressive framework using machine learning and hybrid techniques

Detection and prevention of SQLI attacks and developing compressive framework using machine learning and hybrid techniques

SQL Injection Detection Using 2D-Convolutional Neural Networks (2D-CNN)

Using Data Mining Techniques for the Detection of SQL Injection Attacks on Database Systems

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