Development of a Compressive Framework Using Machine Learning Approaches for SQL Injection Attacks

Deriba, Fitsum Gizachew

doi:10.15199/48.2022.07.30

Cited by 15 publications

(14 citation statements)

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“…The development of a compressive framework using ML Approaches for SQLI attacks has been presented in [54]. The paper investigates the most common SQLI attack forms, their mechanisms, and a method of identifying them based on the existence of the SQL query.…”

Section: Related Workmentioning

confidence: 99%

“…According to the recommendation of [54], the detection and prevention rate of the system can be improved by increasing the training and testing dataset and by using the recommended techniques. So, we have proposed this research work to increase the systems detection and prevention rate of SQLI attacks in different web applications.…”

Section: Related Workmentioning

confidence: 99%

“…This attack grants attackers complete access to databases containing sensitive information [1,10,57,58]. As in a common understanding, a web application has three levels [54]: The first presentation layer collects user feedback and shows the user the processing results. The presentation layer directly communicates with the user.…”

Section: Sqli Query Attacks Overviewmentioning

confidence: 99%

“…These techniques are used for web application vulnerability Fig. 2 Web application architecture [54] analysis, scanning, detection and prevention, mitigation, and attack avoidance. Numerous studies have been conducted to investigate vulnerability analysis by conducting a thorough examination of a web application's security flaws [74].…”

Section: Existing Techniques For Sqli Detection and Preventionmentioning

confidence: 99%

“…If the attacker was granted permission based on the request, he will request application server access again. However, in this case, we proposed a model Table 6 Common inference query attack [54] Types of attack…”

Section: Proposed Framework For Sqli Detection and Preventionmentioning

confidence: 99%

See 4 more Smart Citations

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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%