Abstract
With the development of the Internet, the problem of spam has become more and more prominent. Attackers can spread viruses through spam or place malicious advertisements, which have seriously interfered with people’s life and internet security. Therefore, it is of great significance to study efficient spam detection methods. Currently using machine learning methods for spam detection has become a mainstream direction. In this paper, the machine learning method of Bayesian linear regression and decision forest regression are used to conduct experiments on a data set from UCI Machine Learning Repository. We use the trained models to predict whether a mail is spam or not, and find better prediction scheme by comparing quantitative results. The experimental results show that the method of decision forest regression can get better performance and is suitable for numerical prediction.
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
Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on twitter. In: Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (CEAS), p. 12, July 2010
Fu, Q., Feng, B., Guo, D., Li, Q.: Combating the evolving spammers in online social networks. Comput. Secur. 72, 60–73 (2018)
Youn, S., Cho, H.C.: Improved spam filter via handling of text embedded image e-mail. J. Electr. Eng. Technol. 10(1), 401–407 (2015)
Li, S., et al.: WAF-based chinese character recognition for spam image filtering. Chin. J. Electron. 27(5), 1050–1055 (2018)
Rusland, N.F., Wahid, N., Kasim, S., Hafit, H.: Analysis of Naïve Bayes algorithm for email spam filtering across multiple datasets. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing, August 2017
Wei, Q. Understanding of the naive Bayes classifier in spam filtering. In: AIP Conference Proceedings. AIP Publishing (2018)
Yang, J., Liu, Y., Liu, Z., Zhu, X., Zhang, X.: A new feature selection algorithm based on binomial hypothesis testing for spam filtering. Knowl.-Based Syst. 24(6), 904–914 (2011)
Feng, L., Wang, Y., Zuo, W.: Quick online spam classification method based on active and incremental learning. J. Intell. Fuzzy Syst. 30(1), 17–27 (2016)
Gao, C.Z., Cheng, Q., He, P., Susilo, W., Li, J.: Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack. Inf. Sci. 444, 72–88 (2018)
He, L.: Identification of product review spam by random forest. J. Chin. Inf. Process. 29(3), 150–154 (2015)
Al-Janabi, M., Andras, P.: A systematic analysis of random forest based social media spam classification. In: Yan, Z., Molva, R., Mazurczyk, W., Kantola, R. (eds.) Network and System Security. NSS 2017. LNCS, vol. 10394, pp. 427–438. Springer, Cham. https://doi.org/10.1007/978-3-319-64701-2_31
Sun, X., Han, L., Li, K.: One email filtering system based on category feature selection and feedback learning random forest algorithm. Comput. Appl. Softw. 32(4), 67–71 (2015)
Huawei Cloud. https://support.huaweicloud.com/algnoderef-mls/mls_02_0054.html. Accessed 05 July 2019
Acknowledgments
This research was supported by CERNET Innovation Project (NGII20180407).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, H., Dai, B., Yang, D. (2019). A Comparative Study of Two Different Spam Detection Methods. In: Wang, G., Bhuiyan, M.Z.A., De Capitani di Vimercati, S., Ren, Y. (eds) Dependability in Sensor, Cloud, and Big Data Systems and Applications. DependSys 2019. Communications in Computer and Information Science, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-15-1304-6_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-1304-6_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1303-9
Online ISBN: 978-981-15-1304-6
eBook Packages: Computer ScienceComputer Science (R0)