Authors :
Chetan N; Surya J; Yogananda V; Dr. Vinay K
Volume/Issue :
Volume 10 - 2025, Issue 7 - July
Google Scholar :
https://tinyurl.com/45jhm6sp
Scribd :
https://tinyurl.com/yc8yemp3
DOI :
https://doi.org/10.38124/ijisrt/25jul1755
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 30 to 40 days to display the article.
Abstract :
Email spam has become a major problem in the modern world as a result of the sharp rise in internet users.
These emails are frequently used for unethical and illegal purposes, such as fraud and phishing. Through these emails,
spammers disseminate dangerous links that have the potential to compromise and harm our systems. Spammers can
pretend to be real people in their spam messages by creating phony email accounts and profiles with ease. They typically
prey on those who are not aware of these frauds. Therefore, being able to spot phony spam emails is essential. The goal of
this project is to use machine learning techniques to identify such spam. Several machine learning algorithms will be
examined in this paper, applied to our datasets, and the best algorithm will be selected.
References :
- M. Labonne and S. Moran, "Spam-T5: Benchmarking LLMs for Email Spam Detection," in Proceedings of the International Conference on Computational Linguistics (COLING), 2023.
- S. Jamal and H. Wimmer, "Improved Transformer-Based Spam Detection," Journal of Artificial Intelligence Research (JAIR), vol. 35, pp. 120-135, 2023.
- S. Zavrak and S. Yilmaz, "Hybrid Deep Learning for Email Spam Detection," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 6, pp. 987-999, 2022.
- V. S. Tida and S. Hsu, "Universal Spam Detection with Transfer Learning," in Proceedings of the ACM Conference on Machine Learning (ACM-ML), pp. 230-242, 2022.
- Narur, H. Jain, G. S. Rao, et al., "ML-Based Spam Mail Detector," Springer Journal of Machine Learning and Applications, vol. 27, pp. 89-104, 2023.
- M. Al-Sarem, M. Al-Hadhrami, A. Alshomrani, et al., "Deep Learning for Spam Detection," Expert Systems with Applications, Elsevier, vol. 167, pp. 113872, 2021.
- M. A. Shafi, H. Hamid, E. G. Chiroma, J. S. Dada, and B. Abubakar, "Machine Learning for Email Spam Filtering: Review, Approaches and Open Research Problems," in Proceedings of the International Conference on Artificial Intelligence and Machine Learning (AIML), pp. 45-56, 2018.
- M. Almeida, T. A. Almeida, and A. Silva, "Spam Email Detection Using Deep Learning Techniques," in Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 92-105, 2021.
- M. Madhukar and S. Verma, "Hybrid Semantic Analysis of Tweets: A Case Study of Tweets on Girl-Child in India," Engineering, Technology & Applied Science Research, vol. 7, no. 5, pp. 2014–2016, Oct. 2017.
- C. Bansal and B. Sidhu, ‘‘Machine learning based hybrid approach for email spam detection,’’ in Proc. 9th Int. Conf. Rel., INFOCOM Technol. Optim., Sep. 2021, pp. 1–4.
- Le, H. V., Nguyen, M. T., & Nguyen, T. T. (2018).
- Email spam detection based on ensemble learning of extreme learning machine. International Journal of Machine Learning and Cybernetics, 9(4), 591-602.
- Sahın, Esra, Murat Aydos, and Fatih Orhan. "Spam/ham e-mail classification using machine learning methods based on bag of words technique." 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE.
Email spam has become a major problem in the modern world as a result of the sharp rise in internet users.
These emails are frequently used for unethical and illegal purposes, such as fraud and phishing. Through these emails,
spammers disseminate dangerous links that have the potential to compromise and harm our systems. Spammers can
pretend to be real people in their spam messages by creating phony email accounts and profiles with ease. They typically
prey on those who are not aware of these frauds. Therefore, being able to spot phony spam emails is essential. The goal of
this project is to use machine learning techniques to identify such spam. Several machine learning algorithms will be
examined in this paper, applied to our datasets, and the best algorithm will be selected.