A Comprehensive Model for Spam Detection and Phishing Link Detection

Shilpi Jain, Madhur Jain, Ridhi Kalia and Divyansh Rampal

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 3, 349-353

Abstract: The rapid evolution of technology in recent decades has brought about unprecedented connectivity, efficiency gains, and innovations across various sectors. However, this digital advancement has given rise to new and complex challenges, particularly cybersecurity. This paper presents our project for detecting spam messages/content and phishing links using machine learning algorithms. By comparing various algorithms, we aim to accurately identify and classify suspicious websites and content to mitigate malicious attempts. Our methodology involves training the model on a comprehensive dataset comprising known instances of phishing websites and spam content. Subsequently, the model is employed to classify new websites and content based on their distinct features and characteristics. We exhibit the efficacy of our method by comprehensive testing and assessment on an actual dataset.

Keywords: Dataset; Cybersecurity; Machine Learning (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24103109
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i3:id:210

DOI: 10.32628/CSEIT24103109

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