Phishing URL Detection using Bayesian Optimized Random Forest Classifier

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.7 · Issue 12

Published

July 2019

Access

Open Access

Licence

CC BY-NC-SA 4.0

Vijaypal Singh Rana Rahul Joshi

Internet scams are numerous and varied. Anyone is likely to be the target of an attack while browsing the net. More and more crooks do not hesitate to use Social Engineering as a lever to acquire sensitive data unfairly by exploiting human flaws. Phishing is a Social Engineering technique used by these hackers. It is used to steal personal information in order to commit an identity theft without the knowledge of their victims. The persuasion power of these crooks is the keystone of a successful attack. This paper presents a model with the highest precision results which consists of Bayesian optimized support vector machine classifier. The performance of proposed framework is evaluated using accuracy, precision and sensitivity.

Bayesian Optimization Phishing URL Random Forest Classifier

Vijaypal Singh Rana, Rahul Joshi (2019). Phishing URL Detection using Bayesian Optimized Random Forest Classifier. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.7, Issue 12. ISSN: 2319-4863.

Copy Citation

Full references are available in the PDF version of this paper.

Download Full Paper (PDF) →

Downloads

Full Text Access

Full Paper

PDF Download

Abstract PDF

PDF Download

Article Info

LinkedIn Twitter / X WhatsApp