An Approach Based on SVM Classifier to Detect SQL Injection Attack

Authors(2) :-Ritu Awasthi, Dharmendra Mangal

The query process allows the attacker to achieve uncertified access to the back-end server and database, and remove or change sensitive information. It could be threatened because of interaction of code and data. SQL-Injection, cross-site scripting (XSS), cross-site request forgery (XSRF) are some examples of vulnerabilities. Injection Attacks exploit vulnerabilities of Web pages by inserting and executing malicious. We are proposing SVM (Support Vector Machine) for grouping and prediction of SQL Injection attacks. SQL Injection attack identification or detection accuracy is considerably better among the existing SQL-Injection detection techniques. The proposed framework reduces radically the runtime monitoring overhead. It is focusing only on SQL query conditions and program fragments that are vulnerable to injection attacks.

Authors and Affiliations

Ritu Awasthi
Information Technology, Medi-Caps Institute of Technology and Management, Indore, Madhya Pradesh, India
Dharmendra Mangal
Information Technology, Medi-Caps Institute of Technology and Management, Indore, Madhya Pradesh, India

SQL Injection, Support Vector Machine, Malicious Code, Information Security.

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Publication Details

Published in : Volume 2 | Issue 3 | May-June 2016
Date of Publication : 2016-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 256-260
Manuscript Number : IJSRSET1622417
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Ritu Awasthi, Dharmendra Mangal , " An Approach Based on SVM Classifier to Detect SQL Injection Attack, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 3, pp.256-260, May-June-2016.
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