A Comprehensive Study on SQL Injection Attacks, Their Mode, Detection and Prevention

Dasmohapatra, Sabyasachi; Priyadarshini, Sushree Bibhuprada B.

doi:10.1007/978-981-16-3346-1_50

Cited by 1 publication

(4 citation statements)

References 6 publications

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“…For classification models, the evaluation criteria are precision, recall, f1-score, and training/test set accuracy as described in Eqs. (1)(2)(3)(4). Since positive and negative sample imbalance is very common in the field of SQLI attack detection and prevention, it is unreasonable to use only accuracy rate as the evaluation metric, so the evaluation metric used is f1-score as the detection and prevention classifier performance in addition to detection accuracy (accuracy), check-all rate (recall), and check-accuracy rate (precision).…”

Section: Resultsmentioning

confidence: 99%

“…A comprehensive study on SQLI attacks, their mode, detection, and prevention has been presented in [4]. The authors have identified how attackers of this kind might exploit such a weakness and execute weak code as well as a strategy to mitigate such detrimental effects on database systems.…”

Section: Related Workmentioning

confidence: 99%

“…Several techniques for detecting and preventing SQLI have been proposed, with some focusing on statistical analysis [4,66,67,68,69,70] or dynamic analysis [71,72], others on Hybrid approach [32,73]. These techniques are used for web application vulnerability Fig.…”

Section: Existing Techniques For Sqli Detection and Preventionmentioning

confidence: 99%

“…SQLI attacks on data-driven web applications and systems, also known as SQLI attacks, have been a serious problem since it became common for internet web applications and SQL databases to be connected [4,5,6]. An SQLI attack occurs when an attacker takes advantage of a flaw in the web application's SQL implementation by submitting a malicious SQL statement through a fillable field.…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%