Phishing URL Detection Using Machine Learning

References

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[14] Dataset, http://data.phishtank.com/data/online-valid.xml

[15] Dataset, https://www.kaggle.com/

@article{1705486,
      author = {Ritesh Mourya, Ahmad Raja Khan, Poonam Jain, Dr. S. K Singh},
      title = {Phishing URL Detection Using Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {8},
      pages = {198-202},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705486.pdf},
      abstract = {Phishing is a cybercrime tactic used by malicious actors to deceive individuals or organizations into revealing sensitive information, such as usernames, passwords, credit card numbers, or other personal and financial data. This is done by an attacker by creating a replica of an existing website. This replica is an exact look-alike of famous websites that online users may look for. The term "phishing" is a play on the word "fishing" because it involves luring victims in a similar way to how a fisherman lures fish with bait. Phishing is a prevalent and persistent threat in the digital age, so it's essential to remain cautious and informed to protect your personal and financial information from falling into the wrong hands. In this research, we proposed a method to classify the Uniform Resource Locator (URL) into phishing, suspicious, and non-phishing URLs. This research aims to find the best method for finding a phishing URL when the dataset is in huge numbers. There are many challenges people face when detecting phishing URLs using machine learning algorithms. Protecting users from phishing attacks is vital to maintaining trust and confidence in online services and platforms. Compliance with data protection regulations and industry standards requires effective phishing URL detection to ensure the security of user information.},
      keywords = {Cyber Security, Machine Learning, Phishing Detection, URL},
      month = {February},
  }