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Runtime Phishing URL Detection using Heuristics and Machine Learning

by Md. Aminul Hoque Shamim
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 107
Year of Publication: 2026
Authors: Md. Aminul Hoque Shamim
10.5120/ijcad6515f227fc5

Md. Aminul Hoque Shamim . Runtime Phishing URL Detection using Heuristics and Machine Learning. International Journal of Computer Applications. 187, 107 ( May 2026), 1-9. DOI=10.5120/ijcad6515f227fc5

@article{ 10.5120/ijcad6515f227fc5,
author = { Md. Aminul Hoque Shamim },
title = { Runtime Phishing URL Detection using Heuristics and Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { May 2026 },
volume = { 187 },
number = { 107 },
month = { May },
year = { 2026 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number107/runtime-phishing-url-detection-using-heuristics-and-machine-learning/ },
doi = { 10.5120/ijcad6515f227fc5 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-05-21T00:17:02.054000+05:30
%A Md. Aminul Hoque Shamim
%T Runtime Phishing URL Detection using Heuristics and Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 107
%P 1-9
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

RedKit is a Security as a Service (SECaaS) platform designed to facilitate penetration testing across the entire life cycle through a microservices architecture. RedKit includes both manual and automated testing. In manual testing, RedKit offers custom-built Docker containers as a pre-configured environ-ment that includes a web proxy ready for testing. In automated testing, RedKit features an AI-driven vulnerability scanner to automate repetitive tests, reducing the effort required of penetra-tion testers. RedKit includes information gathering, reconnais-sance tools, and AI report generation. RedKit integrates all these features into a cloud-based, all-in-one framework, a low-effort solution for end-to-end security assessments. By integrat-ing Docker and merging automated testing with manual testing, RedKit builds a full penetration testing framework with mini-mal resource overhead. By accomplishing 60% of resource management and 90% of time saving for setting up the envi-ronment.

References
  1. S. Vidros, C. Kolias, G. Kambourakis, and L. Akoglu. Automatic detection of online recruitment frauds: Characteristics, methods, and a public dataset. Future Internet, 9(1):6, Mar 2017.
  2. S. Lal, R. Jiaswal, N. Sardana, A. Verma, A. Kaur, and R. Mourya. Orfdetector: Ensemble learning based online recruitment fraud detection. In 2019 12th International Conference on Contemporary Computing (IC3), pages 1–5, Noida, India, Aug 2019.
  3. J. Lee and M. J. Cho. Online job scams: Unveiling the impact of overconfidence, digital literacy, and algorithmic literacy on user susceptibility to false job advertisements. New Media & Society, 2025.
  4. V. Anbarasu, S. Selvakani, and M. K. Vasumathi. Fake job prediction using machine learning. Ubiquity, 13(1):12–20, 2024.
  5. R. Rofik, R. A. Hakim, J. Unjung, B. Prasetiyo, and M. A. Muslim. Optimization of svm and gradient boosting models using gridsearchcv in detecting fake job postings. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 23(2):419–430, 2024.
  6. A. S. Pillai. Detecting fake job postings using bidirectional lstm. International Research Journal of Modern Engineering and Technology Science, 5(3):1825–1830, Mar 2023.
  7. S. Chavhan, R. C. Dharmik, and S. Jain. Evaluation of cnnbigru and cnn-bilstm model for fake job post detection: A deep learning approach. In 2024 2nd International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS), 2024.
  8. S. Badere et al. An intelligent system for identifying fake job ads using cnn-bigru and cnn-bilstm. In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE), 2024.
  9. S. S. Sanisetty, S. V. Kotamaraja, B. N. Reddy, and S. Vekkot. Comprehensive approach to fraudulent job post detection using machine learning and bert models. In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), 2025.
  10. K. Taneja, J. Vashishtha, and S. Ratnoo. Fraud-bert: Transformer based context aware online recruitment fraud detection. Discover Computing, 28(1):9, 2025.
  11. C. Srikanth, M. Rashmi, S. Ramu, and R. M. Guddeti. A novel fake job posting detection: An empirical study and performance evaluation using ml and ensemble techniques. In International Conference on Security, Privacy and Data Analytics, 2022.
  12. F. G. Hussain et al. Fake news detection landscape: Datasets, data modalities, ai approaches, their challenges, and future perspectives. IEEE Access, 2025.
  13. A. Papasavva et al. Applications of ai-based models for online fraud detection and analysis. Crime Science, 14(1):7, 2025.
  14. S. Safdar and M. Wasim. Dfn-scnc: Detecting fake news based on social context and news content: A hybrid approach using bert and bi-gru. In 2024 International Conference on Frontiers of Information Technology (FIT), 2024.
  15. M. Wasim et al. Keepup: A unified framework fusing knowledge extraction, social platform engagement, and user profiling for fake news detection. Array, 29:100687, 2026.
  16. K. Yu, S. Jiao, and Z. Ma. Fake news detection based on bert multi-domain and multi-modal fusion network. Computer Vision and Image Understanding, 252:104301, 2025.
  17. L. Caruccio et al. Identifying fake reviews for refund purposes: Evaluating the effectiveness of a transfer-learning model against emerging large language models. Engineering Applications of Artificial Intelligence, 162:112448, 2025.
  18. R. Gupta, I. Kashyap, and V. Jindal. Sbilm: Siamese bi-lstm model for handling imbalance in fake review detection. Procedia Computer Science, 235:1157–1166, 2024.
  19. L. Das, L. Ahuja, and A. Pandey. A novel deep learning model-based optimization algorithm for text message spam detection. The Journal of Supercomputing, 80(12):17823– 17848, 2024.
  20. Ozgur Koray Sahingoz, Ebubekir Buber, Onder Demir, and Banu Diri. Machine learning based phishing detection from urls. Expert Systems with Applications, 117:345–357, 2019.
  21. Rundong Yang, Kangfeng Zheng, Bin Wu, Chunhua Wu, and Xiujuan Wang. Phishing website detection based on deep convolutional neural network and random forest ensemble learning. Sensors, 21(24):8281, 2021.
  22. Hajar Fares, Jihad Kilani, FatimaEzzahra Fagroud, Hicham Toumi, Fatima Lakrami, Youssef Baddi, and Noura Aknin. Machine learning approach for email phishing detection. Procedia Computer Science, 251:746–751, 2024.
  23. Aqilla Khairunnisya, Lindawati Lindawati, and Suzan Zefi. Phishing url detection system using random forest and gradient boosting for cybercrime prevention. CSRID (Computer Science Research and Its Development Journal), 17(3):296– 310, 2025.
Index Terms

Computer Science
Information Sciences

Keywords

Phishing URL Machine Learning Random Forest Support Vector Machines Gradient Boosting

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