Phish Guard Phishing Website using Machine Learning Algorithms

Abhishek, Jadhao and Lakshmi, Mahindre and Komal, Rahangdale and Vinita, Singh and Prof. Rina, Shipurkar and Prof. Usha, Kosarkar (2024) Phish Guard Phishing Website using Machine Learning Algorithms. International Journal of Trend in Scientific Research and Development, 8 (5). pp. 625-634. ISSN 2456-6470

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Abstract

Phishing attacks pose a significant threat to individuals and organizations, leading to substantial financial and reputational damage. Traditional detection methods, such as blacklists and signature-based techniques, often fall short in identifying sophisticated phishing attempts. This research proposes a comprehensive system that leverages machine learning and deep learning techniques to detect and delete phishing threats in emails and websites. The system integrates multiple modules to analyze email structures, text content, and URLs, ensuring a robust defense against phishing attacks. By employing advanced algorithms like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the system achieves high accuracy in identifying phishing attempts. Experimental results demonstrate the system’s effectiveness in real-world scenarios, significantly reducing the risk of phishing attacks. This study contributes to the field of cybersecurity by providing a scalable and efficient solution for phishing detection and mitigation, paving the way for safer online interactions. The anonymous and uncontrollable framework of the Internet is more vulnerable to phishing attacks. Existing research works show that the performance of the phishing detection system is limited. There is a demand for an intelligent technique to protect users from the cyber-attacks. In this study, the author proposed a URL detection technique based on machine learning approaches. A recurrent neural network method is employed to detect phishing URL. Researcher evaluated the proposed method with 7900 malicious and 5800 legitimate sites, respectively. The experiments’ outcome shows that the proposed method’s performance is better than the recent approaches in malicious URL detection. It is one of the familiar attacks that trick users to access malicious content and gain their information. In terms of website interface and uniform resource locator (URL), most phishing webpages look identical to the actual webpages. Various strategies for detecting phishing websites, such as blacklist, heuristic, Etc., have been suggested.

Item Type:	Article
Subjects:	Q Science > Q Science (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	23 Oct 2024 09:41
Last Modified:	23 Oct 2024 09:41
URI:	http://eprints.umsida.ac.id/id/eprint/14239

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