Detection and Prevention Approach to SQLi and Phishing Attack using Machine Learning
J.Jagadessan1, Akshat Shrivastava2, Arman Ansari3, Laxmi Kanta Kar4, Mukul Kumar5

1Dr. J. Jagadessan, Head of Department, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
2Akshat Shrivastava, Student, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
3Md Arman Ansari, Student, Department of CSE, SRM Institute of Science and Technology Ramapuram, Chennai (Tamil Nadu), India.
4Laxmi Kanta Kar, Student, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
5Mukul Kumar, Student, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.

Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 791-799 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6224048419/19©BEIESP
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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 application attacks are increasing and exploiting the security of users. The flow of our paper goes with the discussion of cyber security attacks to the machine learning algorithms to detect and prevent these types of attacks. This paper also uses an open source Web Application Firewall- Mod Security which is an internet technology helps preventing the attacks. We have discussed the approach of WAF with the algorithms of machine learning to efficiently detect the attacks and secure the user. Machine learning learns the attack from previous attacks according to the previous results and block or bypass the Web Applications Firewall. The paper focuses on SQL Injections and Phishing vulnerabilities and prevent attackers to easily deceive them.
Keywords: Web Application Firewalls (WAF), Machine Learning, Mod Security, Cyber Security attacks, SQL Injections, Phishing.

Scope of the Article: Web Mining