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\title{EnLem: An Ensemble Learning-based Model for Detecting Phishing Websites}



\author[1,1]{Most Nilufa Yeasmin }%
\author[2]{Md. Abu Rumman Refat }%
\author[2]{Bikash Chandra Singh }%
\author[2]{Zulfikar Alom }%
\author[2]{Zeyar Aung }%
\author[2]{Mohammad Abdul Azim }%
\affil[1]{Islamic University: Kushtia}%
\affil[2]{Affiliation not available}%


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  \date{May 07, 2026}


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\begin{abstract}
Phishing involves manipulating individuals into revealing private data,
e.g., user IDs, bank details, and passwords. The observed surge in fraud
is related to increased deception, impersonation, and advanced online
attacks. Thus, effective phishing detection methods are required to
mitigate escalating global phishing threats. Existing methods (e.g.,
heuristics-based, signature-based, and visual similarity-based methods)
attempt to detect phishing sites, and machine learning (ML) and deep
learning (DL) methods are effective in the cybersecurity context in
terms of learning from data, offering insights, and forecasting.
However, independent ML algorithms are limited when handling complex
data, and DL techniques surpass traditional ML methods in terms of
performance but require more data and time. This paper introduces
``EnLeM'',~ an ensemble learning model that yields excellent precision,
i.e., 97.06\textbackslash{}\% and 96.36\textbackslash{}\% for before and
after feature selection, respectively, compared to individual ML/DL
methods. To address computational efficiency, we employ univariate
feature selection. Compared to DL models, this approach yielded
promising results (10.01 s for prefeature selection and 8.72 s for
post-feature selection). The proposed model was assessed against five
conventional ML-based classifiers and two DL-based counterparts to
evaluate pre/post feature selection and execution times. The
experimental results demonstrate EnLeM's outstanding, scalable, and
consistent performance.%
\end{abstract}%



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