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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM24738

A HYBRID MACHINE LEARNING BASED APPROACH OF URL FRAUD DETECTION

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM24738

Volume:

7 |

7 |

Jul 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

RPK

Rampam Pavan Kalyan

MM

Maddirala Mukesh

CH

Chagalamarri Hemamber

DT

Dinesh Thunga

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Abstract

Phishing is widely used to scam people around the world. This attack is mainly used to steal the user’s information such as Debit/Credit card, passwords and other sensitive data. This attack can be enforced on large mob at a time so, anyone can be target. This attack is initiated by sending a fake URLs through email, social media but link appears to be so genuine sent right from original organization Thus, user tempts to enter the information. In this article our team is going to handle this kind of attacks. Using Machine learning techniques, our web app is to going to detect whether it is a phishing link or not

KEYWORDS

Phishing Support Vector Machine Decision Tree classifier Machine Learning Cyber Security.

Cite This Article

Rampam Pavan Kalyan, Maddirala Mukesh, Chagalamarri Hemamber, Dinesh Thunga, "A HYBRID MACHINE LEARNING BASED APPROACH OF URL FRAUD DETECTION," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 7, Jul 2023. https://doi.org/10.55041/IJSREM24738

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Publication Date

7/24/2023

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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