An Improved SQL Injection Attack Detection Model Using Machine Learning Techniques

Abdulmalik, Yazeed

doi:10.11113/ijic.v11n1.300

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…In this study [14], Abdulmalik proposed a model to enhance the effectiveness of SQLI attack detection using ML techniques by combining Dynamic and Static Analysis. The model comprises three phases: Dataset, Static and Dynamic Analysis, and Model Construction.…”

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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“…Ref. [29] proposed a model for SQL injection attack detection based on machine learning techniques implemented in the business logic layer of Web applications. It claimed to improve the efficiency of SQL injection attack detection by extracting semantic features from dynamic and static analysis that could effectively indicate SQL injection attacks.…”

Section: Artificial Intelligence-based Detection Methodsmentioning

confidence: 99%

A Semantic Learning-Based SQL Injection Attack Detection Technology

Lu¹,

Fei²,

Liu³

2023

Electronics

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Over the years, injection vulnerabilities have been at the top of the Open Web Application Security Project Top 10 and are one of the most damaging and widely exploited types of vulnerabilities against web applications. Structured Query Language (SQL) injection attack detection remains a challenging problem due to the heterogeneity of attack loads, the diversity of attack methods, and the variety of attack patterns. It has been demonstrated that no single model can guarantee adequate security to protect web applications, and it is crucial to develop an efficient and accurate model for SQL injection attack detection. In this paper, we propose synBERT, a semantic learning-based detection model that explicitly embeds the sentence-level semantic information from SQL statements into an embedding vector. The model learns representations that can be mapped to SQL syntax tree structures, as evidenced by visualization work. We gathered a wide range of datasets to assess the classification performance of the synBERT, and the results show that our approach outperforms previously proposed models. Even on brand-new, untrained models, accuracy can reach 90% or higher, indicating that the model has good generalization performance.

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“…In this research paper [12], the authors addressed the issue of SQL Injection Attacks (SQLIA) in web applications. The paper proposes a model that utilizes machine learning techniques to improve the efficacy of SQLIA detection by extracting semantic features from SQL statements.…”

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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An Improved SQL Injection Attack Detection Model Using Machine Learning Techniques

Cited by 9 publications

References 11 publications

SQL Injection Detection using Machine Learning: A Review

SQL Injection Detection using Machine Learning: A Review

A Semantic Learning-Based SQL Injection Attack Detection Technology

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

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