Detection of SQL injection based on artificial neural network

Tang, Peng; Qiu, Weidong; Huang, Zheng; Lian, Huijuan; Liu, Guozhen

doi:10.1016/j.knosys.2020.105528

Cited by 85 publications

(42 citation statements)

References 19 publications

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“…McWhirter et al [22] extracted the features of SQL based on the gap-weighted string subsequence kernel in 2018, and used SVM for classification with an accuracy rate of over 92.48%. Many literatures used machine learning for detection [18], [24]. For example, Joshi et al [27] used the naive Bayes classification method for detection in 2014.…”

Section: Detection Of Sql Injection Attacks In Webmentioning

confidence: 99%

Pattern Mining and Detection of Malicious SQL Queries on Anonymization Mechanism

Zheng

Shen

2021

IEEE Access

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With the striking development of big data, individual privacy and data security obtain unprecedented importance. Database anonymization mechanism is created for protecting individual privacy by adding noise to the result of a query, which finds a tradeoff between the privacy and utility of personal data. However, corresponding attacks are emerging continuously resulting in a high risk of individual identification. In this paper, we learn patterns of malicious SQL queries and propose a novel detection method. Association rules are used to mine patterns and features of noise-exploitation attacks, and parse trees are applied to the feature extraction of SQL, thereby we construct feature vectors and input them into the classifiers. At the same time, we also propose a SQL generation method to generate query samples based on a real database for model training and testing. Experiments show that our detection method can significantly prevent noise-exploitation attacks including almost all differential attacks and 91% cloning attacks based on the synthetic dataset, which ensures a strong degree of data utility. INDEX TERMS Database anonymization mechanism, diffix, malicious query detection, SQL parsing, random forest. IV. PATTERNS AND CHARACTERISTICS OF MALICIOUS QUERIES A. MAIN TYPES OF ATTACKS

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Section: Detection Of Sql Injection Attacks In Webmentioning

confidence: 99%

Pattern Mining and Detection of Malicious SQL Queries on Anonymization Mechanism

Zheng

Shen

2021

IEEE Access

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“…The results showed their algorithm not only generated better cases as compared with standard genetic algorithm and the adaptive genetic algorithm but also detected web vulnerabilities with high accuracy. Another study in machine learning is conducted by Tang et al [126] that The statistical analysis of normal and SQL injection data was used to design eight feature types and train a machine-learning model. The accuracy of this model was 99%.…”

Section: ) Miscellaneous Pathsmentioning

confidence: 99%

A Survey on Detection and Prevention of Web Vulnerabilities

Noman¹,

Iqbal²,

Manzoor³

2020

IJACSA

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The Internet provides a vast range of benefits to society and empowers the users in a variety of ways to use web applications. Simply, the internet has become the most transformative and fast-growing technology ever built, but it also brings new security challenges to web services in internet applications because of the scattered and open nature of the internet. A simple vulnerability in the program code could favor/benefit an attacker to obtain unauthorized access and perform adversary actions. Hence, the security of web applications from a hacking attempt is of paramount importance. This paper focuses on a literature survey recapitulating security solutions and major vulnerabilities to promote further research by systemizing the existing methods, on a bigger horizon. The data is collected from an absolute of 86 primary studies that are taken from well-known digital libraries. Different methods comprising secure programming, static, Dynamic, Hybrid analysis, and machine learning classify the data from articles. The quantity of references or the significance of a developing strategy is kept in account while selecting articles. Overall, our survey suggests that there is no way to alleviate all the web vulnerabilities therefore more studies is desirable in the area of web information security. All methods' complexity is addressed and some recommendations regarding when to use the application of given methods are provided. Finally, we typify the experience gained and examine future research openings in web application security.

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“…A novel SQL injection detection method based on neural network was presented in [22]. However, time complexity was higher.…”

Section: Related Workmentioning

confidence: 99%

BIBSQLQC: Brown infomax boosted SQL query clustering algorithm to detect anti-patterns in the query log

Ramakrishnan¹,

Palanisamy²

2020

Turk J Elec Eng & Comp Sci

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Discovery of antipatterns from arbitrary SQL query log depends on the static code analysis used to enhance the quality and performance of software applications. The existence of antipatterns reduces the quality and leads to redundant SQL statements. SQL log includes a large load on the database and it is difficult for an analyst to extract large patterns in a minimal time. Existing techniques which discover antipatterns in SQL query face a lot of innumerable challenges to discover the normal sequences of queries within the log. In order to discover the antipatterns in the log, an efficient technique called Brown infomax boosted SQL query clustering (BIBSQLQC) technique is introduced. Initially, the number of patterns (i.e. queries) are extracted from the SQL query log. After extracting the patterns, the ensemble clustering process is carried out to find out the antipatterns from the given query log. The Brown infomax boost clustering is an ensemble learning method for grouping the patterns by constructing several weak learners. The Brown clustering is used as a weak learner for partitioning the patterns into 'k' number of clusters based on the Euclidean distance measure. Then the weak learner merges the two clusters with maximum information gained to minimize the time complexity. The clustering results of weak learners are combined into strong results with minimal error rate (ER). By this way, the antipattern in the SQL query log is detected with a higher accuracy. Experimental evaluation is conducted with different parameters namely detection accuracy (DA), false positive rate (FPR) and time complexity (TC) using the two SQL query log data-sets (DS). The experimental result shows that, the BIBSQLQC technique achieves higher DA with lower TC and FPR than the conventional methods.

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Detection of SQL injection based on artificial neural network

Cited by 85 publications

References 19 publications

Pattern Mining and Detection of Malicious SQL Queries on Anonymization Mechanism

Pattern Mining and Detection of Malicious SQL Queries on Anonymization Mechanism

A Survey on Detection and Prevention of Web Vulnerabilities

BIBSQLQC: Brown infomax boosted SQL query clustering algorithm to detect anti-patterns in the query log

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