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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Paper Title: A Detection Framework for SQL Injections and Cross Site Scripting
Authors Name: Vamsi Mohan V , Dr. Sandeep Malik
Unique Id: IJSDR1901050
Published In: Volume 4 Issue 1, January-2019
Abstract: Designing secure web applications are most important aspect to avoid SQL injections and Cross Site Scripting (XSS) attacks. XSS vulnerabilities are classified into three types. i.e., Reflected XSS, Stored XSS and Dynamic XSS. From these types of XSS, DOM XSS is different from the two others. There are many researches and detection methods proposed for Reflected XSS and Stored XSS. However, it is not suitable for Dynamic XSS. Due to increase of web applications, the threats are getting increased. XSS often included in OWASP top-10 list from the last decade and hence an appropriate XSS detection method is necessary. In this paper, we propose a detection framework for SQLI and XSS. We introduced Regression Neural Network (RNN). It provides accurate and quick solution to regression, approximation, classification and fitting problems. RNN can be used in system identification of dynamic systems as well as control of dynamic systems. Here we are integrating multi-objective optimization which involves integration of objective formulation from dragon fly optimization (DA) and Genetic algorithm (G A). Integrating of optimization algorithm, crossover and mutation is used instead of alignment process of DA.
Keywords: SQL Injections, Cross Site Scripting, Regression Neural Network, Static Analysis, Dynamic Analysis.
Cite Article: "A Detection Framework for SQL Injections and Cross Site Scripting", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.4, Issue 1, page no.288 - 291, January-2019, Available :http://www.ijsdr.org/papers/IJSDR1901050.pdf
Downloads: 000201541
Publication Details: Published Paper ID: IJSDR1901050
Registration ID:190051
Published In: Volume 4 Issue 1, January-2019
DOI (Digital Object Identifier):
Page No: 288 - 291
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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