PhishCatcher: A Machine Learning-Based Client-Side Defence Against Web Spoofing Attacks

References

[1] Omotosho A, Balogun H, Yi Y. Phishing URL detection and interpretability with machine learning: A cross-dataset approach. Security and Privacy. 2025. https://doi.org/10.1002/spy2.70175

[2] Kavya S, Sumathi D. Staying ahead of phishers: A review of recent advances and emerging methodologies in phishing detection. Artificial Intelligence Review. 2025;58:50. https://doi.org/10.1007/s10462-024-11055-z

[3] Mousavi M, et al. Phishing website detection: An in-depth investigation of feature selection and deep learning. Expert Systems. 2025. https://doi.org/10.1111/exsy.13824

[4] Yoon JH, Buu SJ, Kim HJ. Phishing webpage detection via multi-modal integration of HTML DOM graphs and URL features based on graph convolutional and transformer networks. Electronics. 2024;13(16):3344. https://doi.org/10.3390/electronics13163344

[5] Phishing URL detection using machine learning and deep learning. In: Proceedings of the 2024 IEEE World AI IoT Congress (AIIoT). 2024. https://doi.org/10.1109/AIIoT61789.2024.10579005

[6] An integrated model based on deep learning classifiers and pre-trained transformer for phishing URL detection. Future Generation Computer Systems. 2024;161:269–285. https://doi.org/10.1016/j.future.2024.06.031

[7] Sahingoz OK, Buber E, Demir O, Diri B. Machine learning based phishing detection from URLs. Expert Systems with Applications. 2019;117:345–357. https://doi.org/10.1016/j.eswa.2018.09.029

[8] Li Y, Yang Z, Chen X, Yuan H, Liu W. A stacking model using URL and HTML features for phishing webpage detection. Future Generation Computer Systems. 2019;94:27–39. https://doi.org/10.1016/j.future.2018.11.004

[9] Hasane Ahammad SK, Kale SD, Upadhye GD, Pande SD, Babu EV, Dhumane AV, Jang Bahadur DK. Phishing URL detection using machine learning methods. Advances in Engineering Software. 2022;173:103288. https://doi.org/10.1016/j.advengsoft.2022.103288

[10] Aljofey A, et al. An effective detection approach for phishing websites using URL and HTML features. Scientific Reports. 2022;12:8842. https://doi.org/10.1038/s41598-022-10841-5

[11] Aljabri M, et al. URL phishing detection using machine learning techniques based on URLs lexical analysis. In: Proceedings of the 12th International Conference on Information and Communication Systems (ICICS). 2021. https://doi.org/10.1109/ICICS52457.2021.9464539

[12] URL-based phishing websites detection via machine learning. In: Proceedings of the International Conference on Data Analytics for Business and Industry (ICDABI). 2021. https://doi.org/10.1109/ICDABI53623.2021.9655851

[13] Phishing websites detection using machine learning with URL analysis. In: Proceedings of the IEEE World Conference on Applied Intelligence and Computing (AIC). 2022. https://doi.org/10.1109/AIC55036.2022.9848895

[14] A comparative analysis of machine learning-based website phishing detection using URL information. In: Proceedings of the 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). 2022. https://doi.org/10.1109/PRAI55851.2022.9904055

[15] Phishing detection system through hybrid machine learning based on URL. IEEE Access. 2023;11:36805–36822. https://doi.org/10.1109/ACCESS.2023.3252366

@article{1724408,
      author = {Chithra M. K., Pragati R. Pujar, Sona B, Darshan S., Deepthi N. N.; Bhagyashree Wakde},
      title = {PhishCatcher: A Machine Learning-Based Client-Side Defence Against Web Spoofing Attacks},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {4575-4584},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724408.pdf},
      abstract = {Phishing and web spoofing attacks continue to be a major cybersecurity challenge, deceiving users into revealing confidential information through fraudulent websites that mimic trusted domains. Conventional defences such as server‑side monitoring, heuristic filters, and blacklist‑based detection often fail to identify newly emerging or zero‑day phishing domains, leaving end‑users exposed. To address this gap, PhishCatcher proposes a client‑side defence framework that integrates machine learning classifiers for real‑time detection of spoofed websites. The system extracts a diverse set of discriminative features, including lexical analysis of URLs, structural attributes of HTML content, SSL certificate metadata, and visual similarity measures between suspected and legitimate sites. These features are processed locally on the user’s device, enabling proactive classification without reliance on external threat intelligence feeds. Experimental evaluation demonstrates that PhishCatcher achieves high detection accuracy with low false‑positive rates, while maintaining lightweight computational overhead suitable for browser integration. By combining resilience against zero‑day phishing domains with efficient client‑side deployment, PhishCatcher enhances user trust in online interactions and contributes to a safer digital ecosystem.},
      keywords = {phishing detection; web spoofing; client-side defence; machine learning; zero-day attacks; browser integration; cybersecurity resilience; endpoint protection},
      month = {October},
  }