PishGork- Phishing Website Detection Using  Machine Learning

Mahi Patel; Paridhi Kaigaonkar; Raj Jaiswal; Richa Gogde; Rishi Raj Singh Chauhan

doi:10.36948/ijfmr.2025.v07i02.43466

PishGork- Phishing Website Detection Using Machine Learning

Author(s)	Ms. Mahi Patel, Ms. Paridhi Kaigaonkar, Mr. Raj Jaiswal, Ms. Richa Gogde, Mr. Rishi Raj Singh Chauhan
Country	India
Abstract	Phishing attacks exploit deception to obtain sensitive information by imitating legitimate entities, posing a persis tent cybersecurity challenge. Traditional detection techniques, such as blacklist-based filtering and heuristic analysis, often struggle to identify newly emerging threats and evade sophis ticated obfuscation methods. This research introduces a machine learning-based approach to enhance phishing URL detection by analyzing 30 lexical and structural features extracted from website links. Multiple classification algorithms, including Logis tic Regression, Random Forest, Support Vector Machines, and Gradient Boosting (XGBoost), are evaluated to determine their effectiveness in distinguishing phishing websites from legitimate ones. Experimental results on real-world datasets indicate that the Gradient Boosting classifier achieves the highest accuracy. By enabling real-time detection and adapting to evolving attack strategies, the proposed framework strengthens cybersecurity defenses and mitigates phishing risks more effectively than conventional methods.
Keywords	Phishing Detection, Cybersecurity, Machine Learning, URL Analysis, Gradient Boosting, Real-Time Detec tion, Threat Mitigation
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-30
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.43466
Short DOI	https://doi.org/g9g77n

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International Journal For Multidisciplinary Research

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PishGork- Phishing Website Detection Using Machine Learning

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