Phishing Scam Detection using Machine Learning
Elakya R1, Badri Narayan Mohan2, Vijay Kishore. V3, Keerthivasan R4, Naresh Kumar Solanki5
1Mrs. R. Elakya, Assistant Professor, SRM Institute of Science and Technology, Formerly Known SRM University, Ramapuram Chennai (Tamil Nadu), India.
2Mr. Badri Narayan Mohan, Student, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram Chennai (Tamil Nadu), India.
3Mr. Vijay Kishore, Student, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram Chennai (Tamil Nadu), India.
4Mr. Keerthivasan, Student, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram Chennai (Tamil Nadu), India.
5Mr. Naresh Solanki, Student, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram Chennai (Tamil Nadu), India.
Manuscript received on 24 November 2019 | Revised Manuscript received on 07 December 2019 | Manuscript Published on 14 December 2019 | PP: 114-118 | Volume-9 Issue-1S October 2019 | Retrieval Number: A10231091S19/19©BEIESP | DOI: 10.35940/ijeat.A1023.1091S19
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: As a wrongdoing of utilizing specialized intends to take sensitive data of clients and users in the internet, phishing is as of now an advanced risk confronting the Internet, and misfortunes due to phishing are developing consistently. Recognition of these phishing scams is a very testing issue on the grounds that phishing is predominantly a semantics based assault, which particularly manhandles human vulnerabilities, anyway not system or framework vulnerabilities. Phishing costs. As a product discovery plot, two primary methodologies are generally utilized: blacklists/whitelists and machine learning approaches. Every phishing technique has different parameters and type of attack. Using decision tree algorithm we find out whether the attack is legitimate or a scam. We measure this by grouping them with diverse parameters and features, thereby assisting the machine learning algorithm to edify.
Keywords: Decision Tree Algorithm, Machine Learning, Phishing Scam, Client Sensitive Information.
Scope of the Article: Machine Learning