Vol. 4 No. 2 (2025): April
RESEARCH ARTICLES

Enhanced Suspicious URL Detection in IoT Using an Optimized Hybrid Selection Technique

DOWNLOAD PDF
  • Shaik Muskan,
  • Shaik Ashfaq Hussain,
  • Vaddarapu Amareswari,
  • Thanga Sai Krishna,
  • T Sai Sneha,
  • C Venkata Subbaiah
Shaik Muskan
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
Shaik Ashfaq Hussain
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
Vaddarapu Amareswari
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
Thanga Sai Krishna
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
T Sai Sneha
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
C Venkata Subbaiah
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
DOI: https://doi.org/10.5281/zenodo.15181423

Published 2025-04-09

Keywords

  • Boosting estimators,
  • IoT,
  • Web 5.0,
  • internet

How to Cite

Shaik Muskan, Shaik Ashfaq Hussain, Vaddarapu Amareswari, Thanga Sai Krishna, T Sai Sneha, & C Venkata Subbaiah. (2025). Enhanced Suspicious URL Detection in IoT Using an Optimized Hybrid Selection Technique. International Journal of Computational Learning & Intelligence, 4(2), 432–439. https://doi.org/10.5281/zenodo.15181423

Copyright (c) 2025 Shaik Muskan, Shaik Ashfaq Hussain, Vaddarapu Amareswari, Thanga Sai Krishna, T Sai Sneha, C Venkata Subbaiah

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

The rapid growth of the Internet of Things (IoT) has increased the threat of data breaches from malicious links. Identifying suspicious URLs before access is essential for protecting sensitive information. Machine learning methods are effective in detecting zero-day attacks, but their success relies on the quality and complexity of selected features. Earlier approaches primarily used lexical features for faster detection but failed to provide comprehensive website analysis. Enhancing IoT security requires combining both lexical and page content-based features. Researchers use various Feature Selection Techniques (FSTs) to extract meaningful features. However, high resource demands and complex datasets have led to the development of hybrid FSTs. The proposed hybrid FST integrates a filter-based method with a Genetic Algorithm (GA), enhancing the identification of malicious URLs and links. It leverages diverse feature sets and optimized boosting estimators to improve detection accuracy. The model achieves 99% accuracy while minimizing computational costs. This approach strengthens the security of IoT networks by addressing the limitations of previous methods. Efficient feature selection and boosting techniques ensure quick and  making it ideal for resource-limited IoT devices.
DOWNLOAD PDF

References

  1. Ahmed, S. T., Sandhya, M., & Shankar, S. (2018, August). ICT’s role in building and understanding Indian telemedicine environment: A study. In Information and Communication Technology for Competitive Strategies: Proceedings of Third International Conference on ICTCS 2017 (pp. 391–397). Springer.
  2. Alghamdi, B., & Alharby, F. (2019). An intelligent model for online recruitment fraud detection. Journal of Information Security, 10(3), 155–176.
  3. Alsaedi, M., Ghaleb, F., Saeed, F., Ahmad, J., & Alasli, M. (2022). Cyber threat intelligence-based malicious URL detection model using ensemble learning. Sensors, 22(9), 3373.
  4. Anita, C. S., Nagarajan, P., Sairam, G. A., Ganesh, P., & Deepakkumar, G. (2021). Fake job detection and analysis using machine learning and deep learning algorithms. Revista Gestão Inovação e Tecnologias, 11(2), 642–650.
  5. Busireddy Seshakagari Haranadha Reddy. (2025). Deep learning-based detection of hair and scalp diseases using CNN and image processing. Milestone Transactions on Medical Technometrics, 3(1), 145–5. https://doi.org/10.5281/zenodo.14965660
  6. Busireddy Seshakagari Haranadha Reddy, Venkatramana, R., & Jayasree, L. (2025). Enhancing apple fruit quality detection with augmented YOLOv3 deep learning algorithm. International Journal of Human Computations & Intelligence, 4(1), 386–396. https://doi.org/10.5281/zenodo.14998944
  7. Catak, F. O., Sahinbas, K., & Dörtkardeş, V. (2021). Malicious URL detection using machine learning. In Artificial Intelligence Paradigms for Smart Cyber-Physical Systems (pp. 160–180).
  8. Cook, S. (2023). Malware statistics and facts for 2023. Comparitech. Retrieved from https://www.comparitech.com/antivirus/malware-statistics-facts/
  9. Dutta, S., & Bandyopadhyay, S. K. (2020). Fake job recruitment detection using machine learning approach. International Journal of Engineering Trends and Technology, 68(4), 48–53.
  10. Dwaram, J. R., & Madapuri, R. K. (2022). Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India. Concurrency and Computation: Practice and Experience, 34(27). https://doi.org/10.1002/cpe.7310
  11. FlexJobs. (2015). Survey: More millennials than seniors victims of job scams. Retrieved January 2024 from www.flexjobs.com/blog/post/survey-results-millennials-seniors-victims-job-scams
  12. Gupta, B. B., Yadav, K., Razzak, I., Psannis, K., Castiglione, A., & Chang, X. (2021). A novel approach for phishing URLs detection using lexical-based machine learning in a real-time environment. Computer Communications, 175, 47–57.
  13. Howington, J. (2015). Survey: More millennials than seniors victims of job scams. FlexJobs. Retrieved January 2024 from www.flexjobs.com/blog/post/survey-results-millennials-seniors-victims-job-scams
  14. IBM Security. (2023). IBM Security X-Force Threat Intelligence Index 2023. Retrieved from https://www.ibm.com/reports/threat-intelligence
  15. Kamarudin, M., Nor, R. M., & Ramli, M. (2021). Hybrid feature selection using wrapper and filter methods for intrusion detection systems. Information, 12(5), 198.
  16. Kaur, P. (2015). E-recruitment: A conceptual study. International Journal of Applied Research, 1(8), 78–82.
  17. Kazemian, H. B., & Ahmed, S. (2015). Comparisons of machine learning techniques for detecting malicious webpages. Expert Systems with Applications, 42(3), 1166–1177.
  18. Kotsiantis, S., Kanellopoulos, D., & Pintelas, P. (2006). Handling imbalanced datasets: A review. GESTS International Transactions on Computer Science and Engineering, 30(1), 25–36.
  19. Kumar, A., Satheesha, T. Y., Salvador, B. B. L., Mithileysh, S., & Ahmed, S. T. (2023). Augmented intelligence enabled deep neural networking (AuDNN) framework for skin cancer classification and prediction using multi-dimensional datasets on industrial IoT standards. Microprocessors and Microsystems, 97, 104755. https://doi.org/10.1016/j.micpro.2023.104755
  20. Kumar, S. S., Ahmed, S. T., Flora, P. M., Hemanth, L. S., Aishwarya, J., GopalNaik, R., & Fathima, A. (2021, January). An improved approach of unstructured text document classification using predetermined text model and probability technique. In ICASISET 2020: Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology (p. 378). European Alliance for Innovation.
  21. Lal, S., Jiaswal, R., Sardana, N., Verma, A., Kaur, A., & Mourya, R. (2019). ORFDetector: Ensemble learning based online recruitment fraud detection. In 2019 12th International Conference on Contemporary Computing (IC3) (pp. 1–5). IEEE.
  22. Lokku, C. (2021). Classification of genuinity in job posting using machine learning. International Journal of Research in Applied Science and Engineering Technology, 9(12), 1569–1575.
  23. Madapuri, R. K., & Mahesh, P. C. S. (2017). HBS-CRA: Scaling impact of change request towards fault proneness: Defining a heuristic and biases scale (HBS) of change request artifacts (CRA). Cluster Computing, 22(S5), 11591–11599. https://doi.org/10.1007/s10586-017-1424-0
  24. Nasser, I. M., Alzaanin, A. H., & Maghari, A. Y. (2021). Online recruitment fraud detection using ANN. In Palestinian International Conference on Information and Communication Technology (PICICT) (pp. 13–17). IEEE.
  25. Nindyati, O., & Nugraha, I. G. B. B. (2019). Detecting scam in online job vacancy using behavioral features extraction. In International Conference on ICT for Smart Society (ICISS) (Vol. 7, pp. 1–4). IEEE.
  26. Online Fraud. (2022). Retrieved June 19, 2022, from https://www.cyber.gov.au/acsc/report
  27. Patil, K. K., & Ahmed, S. T. (2014, October). Digital telemammography services for rural India, software components and design protocol. In 2014 International Conference on Advances in Electronics Computers and Communications (pp. 1–5). IEEE.
  28. Patil, V., Kulkarni, U., & Biradar, N. (2019). Hybrid feature selection for detecting phishing websites using machine learning algorithms. Journal of King Saud University–Computer and Information Sciences, 34(2), 378–388.
  29. Qabajeh, I., & Thabtah, F. (2014). An experimental study for assessing email classification attributes using feature selection methods. In 3rd International Conference on Advanced Computer Science and Applications Technology (pp. 125–132).
  30. Raza, A., Ubaid, S., Younas, F., & Akhtar, F. (2022). Fake e-job posting prediction based on advanced machine learning approaches. International Journal of Research Publication Review, 3(2), 689–695.
  31. Report Cyber. (2022). Retrieved June 25, 2022, from https://www.actionfraud.police.uk/
  32. Sahoo, D., Liu, C., & Hoi, S. C. H. (2017). Malicious URL detection using machine learning: A survey. arXiv preprint arXiv:1701.07179.
  33. Singh, K. D., & Ahmed, S. T. (2020, July). Systematic linear word string recognition and evaluation technique. In 2020 International Conference on Communication and Signal Processing (ICCSP) (pp. 545–548). IEEE.
  34. SonicWall. (2023). SonicWall Cyber Threat Report 2023. Retrieved from https://www.sonicwall.com/2023-cyber-threat-report/
  35. Tavallaee, M., Stakhanova, N., & Ghorbani, A. A. (2010). Toward credible evaluation of anomaly-based intrusion-detection methods. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(5), 516–524.
  36. Thabtah, F., Abdelhamid, N., & McCluskey, L. (2016). Phishing detection: A recent intelligent machine learning comparison based on models content and features. Security and Communication Networks, 9(18), 6386–6399.
  37. Vidros, S., Kolias, C., Kambourakis, G., & Akoglu, L. (2017). Automatic detection of online recruitment frauds: Characteristics, methods, and a public dataset. Future Internet, 9(1), 6.