A Comprehensive Model for Spam Detection and Phishing Link Detection

Ms. Shilpi Jain; Dr. Madhur Jain; Ridhi Kalia; Divyansh Rampal

doi:10.32628/CSEIT24103109

Authors

Ms. Shilpi Jain Assistant Professor, Department of Mathematics, ARSD College, University of Delhi, Delhi, India Author
Dr. Madhur Jain Assistant Professor, Department of Information Technology, Bhagwan Parshuram Institute of Technology, Delhi, India Author
Ridhi Kalia Student, Department of Information Technology, Bhagwan Parshuram Institute of technology, Delhi, India Author
Divyansh Rampal Student, Department of Information Technology, Bhagwan Parshuram Institute of technology, Delhi, India Author

DOI:

https://doi.org/10.32628/CSEIT24103109

Keywords:

Dataset, Cybersecurity, Machine Learning

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.

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