An Evaluation on the efficiency of E-Mail Spam Detection Using Naive Bayes Classifier

Gaddam Chakradhar Reddy; Ramanadham Rohith Kumar; Pikkili Siva Kasi; Navuluri Sarath Chandra; R. Pavan Kumar; P. Prabakaran

doi:10.55524/ijirem.2022.9.2.103

Abstract

Nowadays, electronic mail is ubiquitous, being used everywhere from the business sector to the classroom. Emails can be broken down into two distinct categories: ham and spam. Email spam, also known as junk email or unwanted email, is a form of email that can be used to harm any user by wasting his or her time, draining system resources, and stealing sensitive data. Every day, the proportion of spam emails increases dramatically. Today's email and IoT service providers face a large and formidable task in detecting and filtering spam. One of the most prominent and widely-known approaches of detecting and avoiding spam is email filtration. It's also one of the most discussed tactics out there. Several machine learning and deep learning techniques, including Naive Bayes, decision trees, neural networks, and random forests, have been employed to reach this objective. After completing a survey of the available machine learning approaches, this article groups them into the most acceptable categories for usage in spam screening on email and IOT platforms. Accuracy, precision, memory requirements, and other metrics are used to thoroughly assess the methodologies. In the final section, we examine both the overall takeaways and directions future studies could go in.

Keywords

E-Mail Spam Detection, Naive Bayes Classifier, Spam Filtering, Spam Filtering

References

J. Dean, “Large scale deep learning,” in Proceedings of the Keynote GPU Technical Conference, San Jose, CA, USA, 2015.
E. Blanzieri and A. Bryl, E-mail Spam Filtering with Local SVM Classifiers, University of Trento, Trento, Italy, 2008.
J. K. Kruschke and T. M. Liddell, “Bayesian data analysis for newcomers,” Psychonomic Bulletin & Review, vol. 25, no. 1, pp. 155–177, 2018.
K. S. Adewole, N. B. Anuar, A. Kamsin, K. D. Varathan, and S. A. Razak, “Malicious accounts: dark of the social networks,” Journal of Network and Computer Applications, vol. 79, pp. 41–67, 2017.
D. Lee, M. J. Lee, and B. J. Kim, “Deviation-based spam-filtering method via stochastic approach,” EPL (Europhysics Letters), vol. 121, no. 6, Article ID 68004, 2018.
PrasaduPeddi (2019), Data Pull out and facts unearthing in biological Databases, International Journal of Techno-Engineering, Vol. 11, issue 1, pp: 25-32
S. E. Kille, Mapping Between X. 400 and RFC 822, University College Department of Computer Science, London, UK, 1986.
A. Makkar and N. Kumar, “An efficient deep learning-based scheme for web spam detection in IoT environment,” Future Generation Computer Systems, vol. 108, pp. 467–487, 2020.
W.-F. Hsiao and T.-M. Chang, “An incremental cluster-based approach to spam filtering,” Expert Systems with Applications, vol. 34, no. 3, pp. 1599–1608, 2008.
P. Lison, An Introduction to Machine Learning, Language Technology Group, Edinburgh, UK, 2015.
G.-H. Lai, C.-M. Chen, and C.-S. LaihT. Chen, “A collaborative anti-spam system,” Expert Systems with Applications, vol. 36, no. 3, pp. 6645–6653, 2009.

Cites this article as

G. C. Reddy, R. R. Kumar, P. S. Kasi, N. S. Chandra, R. P. Kumar, P. Prabakaran, "An Evaluation on the efficiency of E-Mail Spam Detection Using Naive Bayes Classifier", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-9, Issue-2, Page No-648-652, 2022. Available from: https://doi.org/10.55524/ijirem.2022.9.2.103