Abstract
The upsurge in the number of emails send for varying purposes and the dependency of life on them in some way or the other, has given rise to the detection of fraud email and also to the inventors of these emails. The work helps us to prove the efficiency in the detection of spams and prevents the users from opening such emails. The various perspective that are taken into consideration in the work includes parameter tuning, the division of data set on various test train numbers or ratio. Further, the formulation of the study related to parameters and test train set of problem, comparison by both the popular algorithm is discussed. This work has been done after preprocessing and feature extraction of the data. As many of these spam hampers our security, privacy, and often leads in stealing critical information so the categorization of these in an effective manner keeping into mind various parameters is of utmost importance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Christina, V., Karpagavalli, S., & Suganya, G. (2010). Email spam filtering using supervised machine learning techniques. International Journal of Computational Science and Engineering, 02(09), 3126–3129.
Mendez, J.R., DÃaz, F., Iglesias, E.L., Corchado, J.M. (2006). A comparative performance study of feature selection methods for the anti-spam filtering domain. In Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining, (pp. 106–120). Berlin Heidelberg: Springer.
Sakkis, G., Androutsopoulos, I., Paliouras, G., Karkaletsis, V. (2001). Stacking classifiers for anti-spam filtering of E-mail. In Empirical Methods in Natural Language Processing, (pp. 44–50).
Pelletier, L., Almhana, J., Choulakian, V. (2004). Adaptive filtering of spam. In Second Annual Conference on Communication Networks and Services Research (CNSR’04).
Kumar, P., & Biswas, M. (2017, February). SVM with Gaussian kernel-based image spam detection on textual features. In 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT) (pp. 1–6).
Huamin, F., Xinghua, Y., Biao, L., & Chao, J. (2011, December). A spam filtering method based on multi-modal features fusion. In 2011 Seventh International Conference on Computational Intelligence and Security, (pp. 421–426).
Anayat, S., Ali, A., & Ahmad, H. F. (2004, December). Using a probable weight based Bayesian approach for spam filtering. In Proceedings of INMIC 2004 8th International Multitopic Conference, (pp. 340–345).
Seth, S., & Biswas, S. (2017, December). Multimodal spam classification using deep learning techniques. In 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), (pp. 346–349).
Geng, S., Lv, L., & Liu, R. (2014, August). Spam detection approach based on C-support vector machine and kernel principal-component analysis. In 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, (pp. 231–234).
Almeida, T. A., & Yamakami, A. (2010, July). Content-based spam filtering. In The 2010 International Joint Conference on Neural Networks (IJCNN), (pp. 1–7). IEEE.
Cheng, V., & Li, C. H. (2006, December). Personalized spam filtering with semi-supervised classifier ensemble. In 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings) (WI’06), (pp. 195–201). IEEE.
Feng, W., Sun, J., Zhang, L., Cao, C., & Yang, Q. (2016, December). A support vector machine based naive Bayes algorithm for spam filtering. In 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC), (pp. 1–8). IEEE.
Narayan, R., Rout, J. K., & Jena, S. K. (2018). Review spam detection using semi-supervised technique. In Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, (pp. 281–286). Singapore: Springer.
Rusland, N. F., Wahid, N., Kasim, S., & Hafit, H. (2017, August). Analysis of Naïve Bayes algorithm for email spam filtering across multiple datasets. In Proceedings of the IOP Conference Series: Materials Science and Engineering.
Renuka, D. K., Hamsapriya, T., Chakkaravarthi, M. R., & Surya, P. L. (2011, July). Spam classification based on supervised learning using machine learning techniques. In 2011 International Conference on Process Automation, Control and Computing, (pp. 1–7).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mehrotra, T., Rajput, G.K., Verma, M., Lakhani, B., Singh, N. (2021). Email Spam Filtering Technique from Various Perspectives Using Machine Learning Algorithms. In: Singh, T.P., Tomar, R., Choudhury, T., Perumal, T., Mahdi, H.F. (eds) Data Driven Approach Towards Disruptive Technologies. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-9873-9_33
Download citation
DOI: https://doi.org/10.1007/978-981-15-9873-9_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9872-2
Online ISBN: 978-981-15-9873-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)