Web Security Aware by using Naive Baye’s ML Technique
Manjunatha K M1, M Kempanna2

1Manjunatha K M*, Department of Computer Science, Government Polytechnic, Channasandra, Bengaluru, Karnataka, India.
2Dr M Kempanna, Department of Computer Science, Bangalore Institute of Technology, Bengaluru and Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.
Manuscript received on January 10, 2020. | Revised Manuscript received on January 23, 2020. | Manuscript published on February 10, 2020. | PP: 3222-3230 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1325029420/2020©BEIESP | DOI: 10.35940/ijitee.D1325.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Web applications support many of our daily activities, but they often have security issues, and their accessibility makes them easy to use. This paper presents an analysis for finding vulnerabilities that directly address weak or absent of input validation. We present the techniques for finding security vulnerabilities in Web applications. We implement our proposed system with a machine learning technique (ML technique) to measure the accuracy and provide an extensive evaluation that finds all vulnerabilities in web applications. SQL injection, Cross-Site Scripting (XSS), HTTP and command inj1ection vulnerabilities are addressed in the proposed system and also Naive Bayes ML technique is used to calculate the accurateness. The experimental result shows the technique is more efficient and accurate. 
Keywords: Vulnerability Injection, SQL Injection, XSS (Cross-Site Scripting), HTTP, Command Injection and Naive Baye’s Classifier.
Scope of the Article: Web Technologies