Real-Time Phishing Website Detection using Machine Learning and Updating Phishing Probability with User Feedback
Mitesh M. Adake1, Atharva M. Belekar2, Chinmay U. Ambekar3, Dipika D. Bhaiyya4
1Mitesh M. Adake, Department of Computer Engineering, Pune Institute of Computer Technology, Pune (Maharashtra), India.
2Atharva M. Belekar, Department of Computer Engineering, Pune Institute of Computer Technology, Pune (Maharashtra), India.
3Chinmay U. Ambekar, Department of Computer Engineering, Pune Institute of Computer Technology, Pune (Maharashtra), India.
4Prof. Dipika D. Bhaiyya, Department of Computer Engineering, Pune Institute of Computer Technology, Pune (Maharashtra), India.
Manuscript received on 20 April 2023 | Revised Manuscript received on 28 April 2023 | Manuscript Accepted on 15 May 2023 | Manuscript published on 30 May 2023 | PP: 64-71 | Volume-12 Issue-1, May 2023 | Retrieval Number: 100.1/ijrte.A76080512123 | DOI: 10.35940/ijrte.A7608.0512123
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Abstract: Phishing attacks continue to pose a significant threat to internet users worldwide. Cybercriminals often send phishing links through various channels, such as emails, social media platforms, or text messages, to trick users into disclosing their sensitive information, including passwords, usernames, or credit card details. This stolen information is then used to perpetrate various types of fraud or sold on the dark web for profit. To combat this problem, various machine learning-based solutions have been developed for detecting phishing websites. However, these solutions vary in their effectiveness, with some focusing on URLbased algorithms while others concentrate on website content. This paper proposes a machine learning-based approach to realtime phishing website detection, focusing on the website’s URL, domain, page content, and overall content. The proposed framework will be implemented as a browser plug-in, which can identify phishing risks as users visit websites. The framework integrates several techniques, including blacklist interception, whitelist filtering, and machine learning prediction, to improve accuracy, reduce false alarm rates, and minimise computation times. The proposed approach also incorporates user feedback to update the phishing probability over time, thereby increasing the accuracy of detecting phishing websites. This feedback loop involves users reporting suspected phishing websites to the system, which then updates the phishing probability calculation with new information to improve its accuracy. The significance of this research lies in its ability to provide real-time phishing detection capabilities, which can help protect internet users from falling victim to phishing attacks. Furthermore, the use of machine learning-based algorithms and user feedback ensures that the system is continuously updated to remain effective against new and emerging threats.
Keywords: URL, Phishing, Machine Learning, Cyber Secu-rity, Web Browser Extension
Scope of the Article: Machine Learning