Machine Learning-Based Technique to Detect SQL Injection Attack

Azman, Muhammad Amirulluqman; Marhusin, Mohd Fadzli; Sulaiman, Riza

doi:10.3844/jcssp.2021.296.303

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…Azman et al [20] propose a signature-based SQLI detection system that utilises ML techniques. The system is trained on a dataset of benign and malicious web requests extracted from access log files, and its performance is evaluated on a separate testing dataset.…”

Section: Discussionmentioning

confidence: 99%

SQL Injection Detection using Machine Learning: A Review

Mohammed A M Oudah,

Mohd Fadzli Marhusin

2024

MJoSHT

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SQL injection attacks are critical security vulnerability exploitation in web applications, posing risks to data, if successfully executed, allowing attackers to gain unauthorised access to sensitive data. Due to the absence of a standardised structure, traditional signature-based detection methods face challenges in effectively detecting SQL injection attacks. To overcome this challenge, machine learning (ML) algorithms have emerged as a promising approach for detecting SQL injection attacks. This paper presents a comprehensive literature review on the utilisation of ML techniques for SQL injection detection. The review covers various aspects, including dataset collection, feature extraction, training, and testing, with different ML algorithms. The studies included in the review demonstrate high levels of accuracy in detecting attacks and reducing false positives.

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Section: Discussionmentioning

confidence: 99%

SQL Injection Detection using Machine Learning: A Review

Mohammed A M Oudah,

Mohd Fadzli Marhusin

2024

MJoSHT

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“…Based on the results, IBK is selected as the classifier for SQLIA detection and prevention due to its high accuracy, sensitivity, and specificity, as well as its relatively faster model development time compared to other algorithms. SQL injection is a common and dangerous technique used by hackers to manipulate websites and gain access to sensitive data as claimed by the author of [7]. The research proposes the use of machine learning, specifically a training and testing process, to develop a detector for SQL injection attacks.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving ML Accuracy in SQL Injection Detection using NLP and Feature Engineering.

Jaradat,

Rattrout,

Jayousi

2023

Preprint

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This research proposes the DSQLIA model, which enhances the accuracy of machine learning (ML) algorithms in detecting SQL injection attacks. Traditional rule-based and signature-based methods face limitations in effectively identifying evolving attack techniques. To address this, DSQLIA employs feature engineering and Natural Language Processing (NLP). Relevant features capturing the unique characteristics of SQL injection attacks are identified and created through feature engineering. NLP techniques are applied to analyze the textual content of SQL queries and extract meaningful information for distinguishing between legitimate and malicious queries. The DSQLIA model evaluates different ML algorithms, including decision trees, support vector machines (SVM), and artificial neural networks (ANN), on a dataset. Various performance metrics such as accuracy, precision, recall, and F1-score are used to assess algorithm effectiveness. Results show that the SVM algorithm achieves the highest accuracy of 0.994, followed by the decision tree with 0.975, and the ANN with 0.966. This highlights the improved performance of SVM in accurately classifying SQL queries. By combining feature engineering and NLP techniques, the DSQLIA model enhances ML accuracy in SQL injection detection, offering a valuable approach to mitigating the risks posed by these vulnerabilities in web applications.

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“…Several types of research implied that ML techniques [12,[77][78][79] can be employed to develop vulnerability predictors. The goal, regardless of the technique used, is to learn data associated with injection, which can then be used to predict vulnerability to new injections.…”

Section: Techniquesmentioning

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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Machine Learning-Based Technique to Detect SQL Injection Attack

Cited by 12 publications

References 5 publications

SQL Injection Detection using Machine Learning: A Review

SQL Injection Detection using Machine Learning: A Review

Improving ML Accuracy in SQL Injection Detection using NLP and Feature Engineering.

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

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