Illegitimate Websites Detection Using Deep Learning Framework

Phishing is a crime involving robbery of confidential user data. The phishing websites are aimed at individuals, businesses, and cloud storage and government websites. Hardware- based anti-phishing methods are generally used, but software- based approaches are favored because of costs and operational factors. There is no solution to the problem such as zero-day phishing attacks from current phishing detection approaches. A three-phase attack detection called the Phishing Attack Detector based on Web Crawler was proposed to resolve these problems and precisely detect phishing incidences using recurrent neural network. It includes the input features Web traffic, web content and Uniform Resource Locator (URL) based on the classification of phishing and non-phishing pages

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Publication Details

Journal
International Journal of Advanced Research in Science Communication and Technology
Published
2024-11-11
DOI
https://doi.org/10.48175/ijarsct-22156
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Illegitimate Websites Detection Using Deep Learning Framework

Pravin C. Latane, Vikas Ramdas Takale, Prathamesh Kailas Shirke, Mugdha Govardhan Khobare
International Journal of Advanced Research in Science Communication and Technology
Spam and Phishing Detection
article

Illegitimate Websites Detection Using Deep Learning Framework

Pravin C. Latane, Vikas Ramdas Takale, Prathamesh Kailas Shirke, Mugdha Govardhan Khobare
article en

Abstract

Phishing is a crime involving robbery of confidential user data. The phishing websites are aimed at individuals, businesses, and cloud storage and government websites. Hardware- based anti-phishing methods are generally used, but software- based approaches are favored because of costs and operational factors. There is no solution to the problem such as zero-day phishing attacks from current phishing detection approaches. A three-phase attack detection called the Phishing Attack Detector based on Web Crawler was proposed to resolve these problems and precisely detect phishing incidences using recurrent neural network. It includes the input features Web traffic, web content and Uniform Resource Locator (URL) based on the classification of phishing and non-phishing pages

International Journal of Advanced Research in Science Communication and Technology
Peace, Justice and strong institutions
Openalex Percentile: Top 69%
Spam and Phishing Detection
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