International Journal For Multidisciplinary Research

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Detecting Phishing Links Analysis using Machine Learning

Author(s) K.N.S.B.V.MANJUSHA, D.JAYA KUMARI
Country India
Abstract This proposed strategy for identifying phishing websites employed the Gradient Boosting Classifier model, focusing on various aspects of URL significance. By meticulously extracting and comparing different characteristics between legitimate and phishing URLs, our approach leverages the Gradient Boosting Classifier to identify phishing URLs effectively. The study's findings underscore the successful application of our suggested approach in real-time, demonstrating its ability to distinguish between legitimate and bogus websites. Given the relentless evolution of phishing techniques facilitated by advancing technology, employing anti-phishing methods is imperative. Phishing attacks, which often rely on deceptive websites closely resembling genuine ones in appearance and language, pose a significant threat. Machine learning emerges as a robust tool in thwarting such assaults, offering the ability to discern subtle patterns indicative of malicious intent. Phishing remains a preferred tactic for attackers due to its effectiveness in bypassing traditional security measures. By duping unsuspecting users into clicking seemingly authentic yet malicious links, attackers exploit human vulnerability, highlighting the importance of proactive detection mechanisms. In this context, our utilization of the Gradient Boosting Classifier underscores the efficacy of machine learning in fortifying defenses against phishing attacks. As cyber threats evolve, embracing innovative approaches like machine learning becomes essential in safeguarding against emerging risks.
Keywords Phishing attacks, Machine Learning, Gradient Boost Classifier, URL Features.
Field Computer
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-16
Cite This Detecting Phishing Links Analysis using Machine Learning - K.N.S.B.V.MANJUSHA, D.JAYA KUMARI - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.18870
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.18870
Short DOI https://doi.org/gtvt4b
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