ANALISIS KARAKTERISTIK URL PHISHING DAN LEGITIMATE SEBAGAI INDIKATOR ANCAMAN KEAMANAN SIBER MENGGUNAKAN DATASET PUBLIK
DOI:
https://doi.org/10.62671/suliwa.v3i3.370Keywords:
phishing URL, legitimate URL, Lexical Features, public dataset, CybersecurityAbstract
Phishing URLs can imitate legitimate services and expose users to credential theft. This study compared lexical and structural characteristics of phishing and legitimate URLs using the public PhiUSIIL dataset, which documents 100,945 phishing and 134,850 legitimate URLs. URL strings were parsed offline without visiting their destinations. Eight features were examined: URL length, hostname length, digit proportion, hyphen count, hostname dot count, IP-based hosts, HTTPS scheme, and query presence. After removing 425 duplicate records, 235,370 URLs were analyzed using medians, interquartile ranges, proportions, Mann–Whitney tests, rank-biserial effect sizes, and Holm correction. Phishing URLs showed larger digit proportion (r = 0.45), URL length (r = 0.44), hyphen count (r = 0.30), and host length (r = 0.25), whereas hostname dot count differed negligibly (r = −0.03). HTTPS appeared in 48.6% of phishing URLs versus 100% of legitimate URLs; IP-based hosts and query strings occurred only among phishing URLs. Because all legitimate URLs were HTTPS homepage addresses without paths or queries, these contrasts partly reflect how the classes were built. The features are candidate warning indicators, not deterministic rules.
References
Alabdan, R. (2020). Phishing attacks survey: Types, vectors, and technical approaches. Future Internet, 12(10), 168. https://doi.org/10.3390/fi12100168
Aleroud, A., & Zhou, L. (2017). Phishing environments, techniques, and countermeasures: A survey. Computers & Security, 68, 160–196. https://doi.org/10.1016/j.cose.2017.04.006
Aljofey, A., Jiang, Q., Qu, Q., Huang, M., & Niyigena, J.-P. (2020). An effective phishing detection model based on character level convolutional neural network from URL. Electronics, 9(9), 1514. https://doi.org/10.3390/electronics9091514
Butnaru, A., Mylonas, A., & Pitropakis, N. (2021). Towards lightweight URL-based phishing detection. Future Internet, 13(6), 154. https://doi.org/10.3390/fi13060154
Chiew, K. L., Yong, K. S. C., & Tan, C. L. (2018). A survey of phishing attacks: Their types, vectors and technical approaches. Expert Systems with Applications, 106, 1–20. https://doi.org/10.1016/j.eswa.2018.03.050
Hannousse, A., & Yahiouche, S. (2021). Towards benchmark datasets for machine learning based website phishing detection: An experimental study. Engineering Applications of Artificial Intelligence, 104, 104347. https://doi.org/10.1016/j.engappai.2021.104347
Jain, A. K., & Gupta, B. B. (2018). PHISH-SAFE: URL features-based phishing detection system using machine learning. In Cyber Security (Advances in Intelligent Systems and Computing, Vol. 729). Springer. https://doi.org/10.1007/978-981-10-8536-9_44
Jain, A. K., & Gupta, B. B. (2019). A machine learning based approach for phishing detection using hyperlinks information. Journal of Ambient Intelligence and Humanized Computing, 10, 2015–2028. https://doi.org/10.1007/s12652-018-0798-z
Le, H., Pham, Q., Sahoo, D., & Hoi, S. C. H. (2018). URLNet: Learning a URL representation with deep learning for malicious URL detection [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1802.03162
Potpelwar, R. S., Kulkarni, U. V., & Waghmare, J. M. (2025). LegitPhish: A large-scale annotated dataset for URL-based phishing detection. Data in Brief, 63, 111972. https://doi.org/10.1016/j.dib.2025.111972
Prasad, A., & Chandra, S. (2023). Machine learning to combat cyberattack: A survey of datasets and challenges. The Journal of Defense Modeling and Simulation, 20(4), 577–588. https://doi.org/10.1177/15485129221094881
Prasad, A., & Chandra, S. (2024a). PhiUSIIL phishing URL (website) [Data set]. UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/967/phiusiil+phishing+url+dataset
Prasad, A., & Chandra, S. (2024b). PhiUSIIL: A diverse security profile empowered phishing URL detection framework based on similarity index and incremental learning. Computers & Security, 136, 103545. https://doi.org/10.1016/j.cose.2023.103545
Safi, A., & Singh, S. (2023). A systematic literature review on phishing website detection techniques. Journal of King Saud University – Computer and Information Sciences, 35(2), 590–611. https://doi.org/10.1016/j.jksuci.2023.01.004
Sahingoz, O. K., Buber, E., Demir, O., & Diri, B. (2019). Machine learning based phishing detection from URLs. Expert Systems with Applications, 117, 345–357. https://doi.org/10.1016/j.eswa.2018.09.029
Srivastava, P., Singh, A., Virmani, A., Shukla, H., Mishra, R. K., Agarwal, R., & Mishra, A. K. (2026). A framework for real-time phishing resource locator detection using structural and lexical feature analysis based on machine learning. International Journal of Science and Engineering Science Research, 2(4), 24–33. https://doi.org/10.63671/ijsesr.v2i4.171
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