Employing Neural Networks for the detection of SQL injection attack

Naghmeh Moradpoor Sheykhkanloo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
38 Downloads (Pure)

Abstract

Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into the SQL database by simply using web browsers. SQLI attack can cause severe damages on a given SQL database such as losing data, disclosing confidential information or even changing the values of data. It has also been rated as the number-one attack on the Open Web Application Security Project (OWASP) top ten. In this paper, we propose an effective model to deal with this problem based on Neural Networks (NNs). The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to classify the generated URLs to either benign or malicious URLs, and an NN model in order to detect either a given URL is a malicious URL or a benign URL. The model is first trained and then evaluated by employing both benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.
Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Security of Information and Networks
EditorsRon Poet
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages318-323
Number of pages6
ISBN (Print)9781450330336
DOIs
Publication statusPublished - 9 Sep 2014
Event7th International Conference on Security of Information and Networks - University of Glasgow, Glasgow, United Kingdom
Duration: 9 Sep 201411 Sep 2014
Conference number: 7th

Conference

Conference7th International Conference on Security of Information and Networks
Abbreviated titleSIN 2014
CountryUnited Kingdom
CityGlasgow
Period9/09/1411/09/14

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Websites
Neural networks
Query languages
Web browsers
Classifiers

Cite this

Moradpoor Sheykhkanloo, N. (2014). Employing Neural Networks for the detection of SQL injection attack. In R. Poet (Ed.), Proceedings of the 7th International Conference on Security of Information and Networks (pp. 318-323). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/2659651.2659675
Moradpoor Sheykhkanloo, Naghmeh. / Employing Neural Networks for the detection of SQL injection attack. Proceedings of the 7th International Conference on Security of Information and Networks. editor / Ron Poet. New York : Association for Computing Machinery (ACM), 2014. pp. 318-323
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Moradpoor Sheykhkanloo, N 2014, Employing Neural Networks for the detection of SQL injection attack. in R Poet (ed.), Proceedings of the 7th International Conference on Security of Information and Networks. Association for Computing Machinery (ACM), New York, pp. 318-323, 7th International Conference on Security of Information and Networks, Glasgow, United Kingdom, 9/09/14. https://doi.org/10.1145/2659651.2659675

Employing Neural Networks for the detection of SQL injection attack. / Moradpoor Sheykhkanloo, Naghmeh.

Proceedings of the 7th International Conference on Security of Information and Networks. ed. / Ron Poet. New York : Association for Computing Machinery (ACM), 2014. p. 318-323.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Moradpoor Sheykhkanloo N. Employing Neural Networks for the detection of SQL injection attack. In Poet R, editor, Proceedings of the 7th International Conference on Security of Information and Networks. New York: Association for Computing Machinery (ACM). 2014. p. 318-323 https://doi.org/10.1145/2659651.2659675