SQL Injection Vulnerabilities Prevention through ML IPAAS Architecture
Machine Learning, SQL, architecture
XSS and SQL infusion vulnerabilities both show at a crucial level as an inability to safeguard the honesty of HTML reports and SQL inquiries, individually, within the sight of untrusted contribution to the web application. In the former case,an XSS vulnerability allows an attacker to inject dangerous HTML components, regularly including noxious customer side code that executes in the security setting of a confided in web root. In the last case, a SQL infusion injec-tion defenselessness enables an assailant to adjust a current database question — or, now and again, to infuse a totally new one — so that damages the web application's coveted information uprightness or classification strategies. IPAAS consequently and straightforwardly expands generally shaky web application improvement conditions within put validators that result in significant and tangible security improvements for real systems. We implemented IPAAS for PHP and assessed it on five genuine web applications with known XSS and SQL infusion vulnerabilities. Our assessment exhibits that IPAAS would have forestalled 83% of SQL infusion vulnerabilities and 65% of XSS vulnerabilities while causing no developerburden. This paper provides the SQL injection vulnerabilities prevention through ML IPAAS architecture.
"SQL Injection Vulnerabilities Prevention through ML IPAAS Architecture", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.7, Issue 3, page no.832-837, March-2022, Available :https://ijnrd.org/papers/IJNRD2203104.pdf
Volume 7
Issue 3,
March-2022
Pages : 832-837
Paper Reg. ID: IJNRD_180770
Published Paper Id: IJNRD2203104
Downloads: 000118804
Research Area: Engineering
Country: -, -, -
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
