Detection of Phishing Website URLs and Email/SMS Using Random Forest and Multinomial Naive Bayes


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Year : 2025 | Volume : 16 | Issue : 01 | Page : 22-30
    By

    Sridevi Ravada,

  • Harika Kasukurthi,

  • Deekshitha Govindu,

  • Sowmya Vara,

  • Akshaya Enumukkala,

  1. Assistant Professor, Department of Information Technology, Gayatri Vidya Parishad College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  2. Student, Department of Information Technology, Gayatri Vidya Parishad College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  3. Student, Department of Information Technology, Gayatri Vidya Parishad College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  4. Student, Department of Information Technology, Gayatri Vidya Parishad College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  5. Student, Department of Information Technology, Gayatri Vidya Parishad College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India

Abstract

Currently, phishing attacks via SMS/email and URL have become significant threat to cybersecurity, posing risks to both individuals and organizations alike. Phishing attacks typically involve the creation of fraudulent websites or the dissemination of deceptive emails and SMS messages to trick users into disclosing sensitive information such as passwords, credit card numbers or personal details. To respond to these attacks, we develop a robust system for the detection of phishing URLs and SMS/emails using machine learning techniques. For phishing website URL detection, we extracted various features from HTML content using Beautiful Soup, and then applied supervised learning algorithms such as Decision Trees, Random Forest, and XGBoost, and Multinomial Naïve Bayes to classify URLs as Legitimate or Phishing. We achieved promising results with the Random Forest, which demonstrated high accuracy in distinguishing between legitimate and phishing URLs. For email/SMS, TF-IDF Vectorization, Natural language preprocessing is used and then applied supervised learning algorithms such as Multinomial Naïve Bayes, support vector classifier (SVC), Random Forest, Decision Tree, AdaBoost and XGBoost. We achieved promising results with the Multinomial Naïve Bayes, which demonstrated high accuracy in distinguishing between spam and not spam.

Keywords: Phishing attacks, multinomial Naïve Bayes, TF-IDF vectorization, natural language preprocessing, random forest, beautiful soup

[This article belongs to Journal of Computer Technology & Applications (jocta)]

How to cite this article:
Sridevi Ravada, Harika Kasukurthi, Deekshitha Govindu, Sowmya Vara, Akshaya Enumukkala. Detection of Phishing Website URLs and Email/SMS Using Random Forest and Multinomial Naive Bayes. Journal of Computer Technology & Applications. 2024; 16(01):22-30.
How to cite this URL:
Sridevi Ravada, Harika Kasukurthi, Deekshitha Govindu, Sowmya Vara, Akshaya Enumukkala. Detection of Phishing Website URLs and Email/SMS Using Random Forest and Multinomial Naive Bayes. Journal of Computer Technology & Applications. 2024; 16(01):22-30. Available from: https://journals.stmjournals.com/jocta/article=2024/view=191737


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Regular Issue Subscription Review Article
Volume 16
Issue 01
Received 25/10/2024
Accepted 19/12/2024
Published 31/12/2024
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