NORMA eResearch @NCI Library

Novel Approach to Detect SQL Injection Attacks

Chaudhary, Chirag (2021) Novel Approach to Detect SQL Injection Attacks. Masters thesis, Dublin, National College of Ireland.

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The concerns for the cyber security threats have increased drastically since the increase in the use of the online services which are web-based applications. To increase the customer base most of the organizations are providing with more and better services through the online platform and giving them access to their web applications. And since the online applications stores sensitive and personal data of the users, if any malicious individual are able to attain unauthorized access they can cause serious harm. These web applications use databases to store the data which can be operated by the use of the SQL language commands. The attackers use this language to gain access and change, delete or steal the data from the database. In this research machine learning algorithms are used to find a mechanism that can detect the SQL injection attacks. Throughout the research the experiments are performed using three major classification models which are – Gradient Boosting algorithm, Random Forests and Support Vector Regression. Further, critical analysis and comparison of each algorithm is implemented to determine the most optimal model that can be used to build the SQL Injection Detection System.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
H Social Sciences > HV Social pathology. Social and public welfare > Criminology > Crimes and Offences > Cyber Crime
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Clara Chan
Date Deposited: 18 Oct 2021 14:25
Last Modified: 18 Oct 2021 14:25

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