@article{4533,
  author = {Arun Kumar R, Vaishnavi H, Anusha S, Nausheen K, Apeksha P},
  title = {Algorithms and Methods for Detection of Phishing Website: A Review},
  journal = {Journal of Information Security Research},
  year = {2025},
  volume = {16},
  number = {3},
  doi = {https://doi.org/10.6025/jisr/2025/16/3/98-109},
  url = {https://www.dline.info/jisr/fulltext/v16n3/jisrv16n3_2.pdf},
  abstract = {Phishing websites pose a serious threat, prompting the use of machine learning for detection. Researchers
employ algorithms like XGBoost, Gradient Boosting, Adaboost, SVM, and Random Forest, trained on datasets
to discern patterns distinguishing phishing from legitimate sites. Feature extraction analyzes URL structure,
domain age, and content, while deep learning automates this process, identifying complex patterns. These
techniques exhibit high accuracy, surpassing rule-based methods. Ongoing research positions machine
learning as a crucial tool against online phishing, showcasing its potential in bolstering cybersecurity.},
}