UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 7
July-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2107809


Registration ID:
313945

Page Number

g615-g624

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Title

Applied Machine Learning to Predict the SQL Injection Attacks

Abstract

In current days for any small scale or large scale enterprise companies, their data plays one of the important assets and this is very important for each and every enterprises. As we all know that as data is increasing day by day there are a lot of attackers who try to create some sort of attacks on that data by injecting some traits. One among the several attacks in enterprise level is SQL injection attack, which will be injecting the fake contents inside the business server and try to alter the queries based on intruder choice. In general it is very difficult for the network administrator to trace the difference between the normal SQL query and Injected SQL query which is triggered for the enterprise server. In this paper we try to design SQL injection detection based on SQL tainting method, which can greatly identify the intruders who try to create SQL attacks on the employee tuples and try to differentiate the genuine query and abnormal query easily in the run time. Our Experimental results clearly state that this approach is best in identifying the attackers dynamically and detect the type of injection strings applied to gather the sensitive information from the business server.

Key Words

SQL Injection, SQL Tainting, Network Administrator, Attacks, Employee Tuples, Intruders.

Cite This Article

"Applied Machine Learning to Predict the SQL Injection Attacks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.g615-g624, July-2021, Available :http://www.jetir.org/papers/JETIR2107809.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Applied Machine Learning to Predict the SQL Injection Attacks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppg615-g624, July-2021, Available at : http://www.jetir.org/papers/JETIR2107809.pdf

Publication Details

Published Paper ID: JETIR2107809
Registration ID: 313945
Published In: Volume 8 | Issue 7 | Year July-2021
DOI (Digital Object Identifier):
Page No: g615-g624
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162


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