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Machine Learning-based Phishing URL Detection using Lexical and Structural Features
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 96 |
| Year of Publication: 2026 |
| Authors: Muneiah Tellakula, Phaneendra Kanduri, Sivamanikanta Reddy Ramireddy, UdaySankar Reddy Konche |
10.5120/ijcaebeeeb7bb333
|
Muneiah Tellakula, Phaneendra Kanduri, Sivamanikanta Reddy Ramireddy, UdaySankar Reddy Konche . Machine Learning-based Phishing URL Detection using Lexical and Structural Features. International Journal of Computer Applications. 187, 96 ( Apr 2026), 1-5. DOI=10.5120/ijcaebeeeb7bb333
Abstract
Phishing attacks, which use fraudulent sites to gather sensitive information from users, continue to be one of the major threats in cybersecurity. Thus, this work proposes a machine learning-oriented method for detecting phishing URL by leveraging the lexical and structural characteristics of URLs to overcome such difficulty. Over 100,000 URLs from the dataset were encoded as sixteen hand-crafted features which contained domain, path and character level information. For detection performance, Random Forest classifier with balanced class weights was used to decrease class imbalance. The aforementioned outcomes from the experiments validate that the proposed model plays a highly effective role in classifying if a given URL is phishing or legal with high accuracy attaining equal precision and recall. The proposed method has lower computing complexity and performs competitively to deep learning techniques, thus its suitability for real time phishing prevention systems.
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