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Journal of Cognitive Human-Computer Interaction

Volume 9 , Issue 2 , PP: 28-33, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Review of Machine Learning Technique based Prediction Model for Phishing Websites Detection

RishiKesh Dube 1 * , Twinkle Sharma 2 , Damodar Tiwari 3 , Kailash Patidar 4

  • 1 Research Scholar, Dept. of CSE, Bansal Institute of Science and Technology, Bhopal, India - (rishikeshdubey6@gmail.com)
  • 2 Assistant Professor, Dept. of CSE, Bansal Institute of Science and Technology, Bhopal, India - (twinklesharma1311@gmail.com)
  • 3 Professor, Dept. of CSE, Bansal Institute of Science and Technology, Bhopal, India - (damodar@bistbpl.in)
  • 4 Professor, Dept. of CSE, Bansal Institute of Science and Technology, Bhopal, India - (Kailashpatidar123@gmail.com)

    • Doi: https://doi.org/10.54216/JCHCI.090204

    Received: January 17, 2025 Revised: February 07, 2025 Accepted: April 06, 2025
    Abstract

    Phishing attacks have emerged as a significant cybersecurity challenge, targeting individuals and organizations by tricking users into revealing sensitive information through deceptive websites. Traditional phishing detection methods, such as blacklists and heuristic-based approaches, struggle to keep pace with the rapid evolution of phishing techniques. Machine learning-based predictive models offer a promising solution by analyzing website attributes, URL structures, and behavioral patterns to distinguish between legitimate and phishing websites. This paper provides a comprehensive review of various machine learning techniques, including decision trees, support vector machines (SVM), random forests, deep learning models, and ensemble methods, employed in phishing website detection. It explores feature selection strategies, dataset characteristics, performance evaluation metrics, and real-world implementation challenges. Furthermore, the study discusses recent advancements such as adversarial resilience, natural language processing (NLP) integration, and real-time phishing detection frameworks. The review highlights existing research gaps and future directions to enhance phishing detection accuracy, scalability, and adaptability in evolving cybersecurity landscapes.

    Keywords :

    Phishing Websites , Machine Learning , Accuracy , NLP  ,

    References

    [1]       L. R. Kalabarige, R. S. Rao, A. R. Pais, and L. A. Gabralla, "A Boosting-Based Hybrid Feature Selection and Multi-Layer Stacked Ensemble Learning Model to Detect Phishing Websites," in IEEE Access, vol. 11, pp. 71180-71193, 2023.

    [2]       Y. Sun, G. Liu, X. Han, W. Zuo, and W. Liu, "FusionNet: An Effective Network Phishing Website Detection Framework Based on Multi-Modal Fusion," in 2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), Melbourne, Australia, 2023, pp. 474-481.

    [3]       S. Mittal, R. Agarwal, M. L. Saini, and A. Kumar, "A Logistic Regression Approach for Detecting Phishing Websites," in 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), Faridabad, India, 2023, pp. 76-81, doi: 10.1109/ICAICCIT60255.2023.10466221.

    [4]       J. M. Lindamulage, M. L, Y. S.P.J, P. I.S.S., and J. Krishara, "Vision GNN Based Phishing Website Detection," in 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India, 2023, pp. 1-7.

    [5]       A. Smith and B. Johnson, "Machine Learning Techniques for Phishing Detection: A Review," International Journal of Computer Applications, vol. 182, no. 5, pp. 1-8, 2024.

    [6]       R. Sultana, M. A. Rahman, and M. Ibrahim Khan, "Hybrid Model Based Phishing Websites Detection Using Deep Learning Technique," in 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2023, pp. 1-6.

    [7]       M. A. Snober, A. Droos, and Q. A. Al-Haija, "Prevention of Phishing Website Attacks in Online Banking Systems Using Visual Cryptography," in 6th Smart Cities Symposium (SCS 2022), Hybrid Conference, Bahrain, 2022, pp. 168-173, doi: 10.1049/icp.2023.0391.

    [8]       P. Jaswal, S. Sharma, N. Bindra, and C. R. Krishna, "Detection and Prevention of Phishing Attacks on Banking Website," in 2022 International Conference on Futuristic Technologies (INCOFT), Belgaum, India, 2022, pp. 1-8, doi: 10.1109/INCOFT55651.2022.10094345.

    [9]       D. Ito, Y. Takata, and M. Kamizono, "Money Talks: Detection of Disposable Phishing Websites by Analyzing Its Building Costs," in 2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA), Atlanta, GA, USA, 2022, pp. 97-106, doi: 10.1109/TPS-ISA56441.2022.00022.

    [10]    C. Lee and D. Kim, "IoT Security: Challenges and Solutions for Smart Devices," Journal of Network and Computer Applications, vol. 202, no. 1, pp. 15-25, 2023.

    [11]    M. M. Uddin, K. Arfatul Islam, M. Mamun, V. K. Tiwari, and J. Park, "A Comparative Analysis of Machine Learning-Based Website Phishing Detection Using URL Information," in 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), Chengdu, China, 2022, pp. 220-224.

    [12]    L. Shalini, S. S. Manvi, N. C. Gowda, and K. N. Manasa, "Detection of Phishing Emails Using Machine Learning and Deep Learning," in 2022 7th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2022, pp. 1237-1243, doi: 10.1109/ICCES54183.2022.9835846.

    [13]   Isatish, "Phishing dataset: A comprehensive collection," Kaggle, 2023. [Online]. Available: https://www.kaggle.com/datasets/isatish/phishing-dataset-uci-ml-csv?select=uci-ml-phishing-dataset.csv

    Cite This Article As :
    Dube, RishiKesh. , Sharma, Twinkle. , Tiwari, Damodar. , Patidar, Kailash. Review of Machine Learning Technique based Prediction Model for Phishing Websites Detection. Journal of Cognitive Human-Computer Interaction, vol. , no. , 2025, pp. 28-33. DOI: https://doi.org/10.54216/JCHCI.090204
    Dube, R. Sharma, T. Tiwari, D. Patidar, K. (2025). Review of Machine Learning Technique based Prediction Model for Phishing Websites Detection. Journal of Cognitive Human-Computer Interaction, (), 28-33. DOI: https://doi.org/10.54216/JCHCI.090204
    Dube, RishiKesh. Sharma, Twinkle. Tiwari, Damodar. Patidar, Kailash. Review of Machine Learning Technique based Prediction Model for Phishing Websites Detection. Journal of Cognitive Human-Computer Interaction , no. (2025): 28-33. DOI: https://doi.org/10.54216/JCHCI.090204
    Dube, R. , Sharma, T. , Tiwari, D. , Patidar, K. (2025) . Review of Machine Learning Technique based Prediction Model for Phishing Websites Detection. Journal of Cognitive Human-Computer Interaction , () , 28-33 . DOI: https://doi.org/10.54216/JCHCI.090204
    Dube R. , Sharma T. , Tiwari D. , Patidar K. [2025]. Review of Machine Learning Technique based Prediction Model for Phishing Websites Detection. Journal of Cognitive Human-Computer Interaction. (): 28-33. DOI: https://doi.org/10.54216/JCHCI.090204
    Dube, R. Sharma, T. Tiwari, D. Patidar, K. "Review of Machine Learning Technique based Prediction Model for Phishing Websites Detection," Journal of Cognitive Human-Computer Interaction, vol. , no. , pp. 28-33, 2025. DOI: https://doi.org/10.54216/JCHCI.090204

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