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ENHANCING PHISHING CLASSIFICATION STRATEGIES USING MACHINE LEARNING

  • Unique Paper ID: 166885
  • Volume: 11
  • Issue: 2
  • PageNo: 2438-2443
  • Abstract:
  • Phishing attacks have evolved into a major cybersecurity concern, prompting extensive research to identify the most effective methods for classifying and detecting these deceptive tactics, which aim to deceive individuals and organizations into revealing sensitive information. This project addresses a notable gap in prior research by systematically evaluating various classification techniques under changing data conditions, ensuring that they are not limited to specific datasets or methods, thus offering a broader perspective on their effectiveness in combating phishing attacks. The study conducted assessments on thirteen contemporary classification techniques that are commonly utilized in preliminary research related to phishing. This comprehensive study examined the efficacy of diverse phishing classification methods, employing a multifaceted evaluation framework comprising ten distinct metrics. The research findings significantly contribute to the existing body of knowledge on phishing detection strategies, offering novel perspectives and valuable implications. By shedding light on the strengths and limitations of various approaches, this study paves the way for the creation of more resilient countermeasures against phishing threats. The project incorporates the Stacking Classifier, a robust ensemble method, combining RF, MLP, and LightGBM models to achieve 100% accuracy in phishing attack classification. A user-friendly Flask- based front end enables easy user testing and performance Evaluation Implemented user authentication ensures secure access, contributing to a comprehensive evaluation of phishing classification techniques across diverse data sources and schemes.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 2
  • PageNo: 2438-2443

ENHANCING PHISHING CLASSIFICATION STRATEGIES USING MACHINE LEARNING

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8.01 (Year 2024)
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Journal no 47859

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