Abstract
The increasing volume of unsolicited bulk e-mails leads to the need for reliable stochastic spam detection methods for the classification of the received sequence of e-mails. When a sequence of emails is received by a recipient during a time period, the spam filters have already classified them as spam or not spam. Due to the dynamic nature of the spam, there might be emails marked as not spam but are actually real spams and vice versa. For the sake of security, it is important to be able to detect real spam emails. This paper utilizes stochastic methods to refine the preliminary spam detection and to find maximum likelihood for spam e-mail classification. The method is based on the Bayesian theorem, hidden Markov model (HMM), and the Viterbi algorithm.
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
Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A Bayesian approach to filtering junk e-mail. In: AAAI 1998 Workshop on Learning for Text Categorization (1998)
Al-Jarrah, O., Khaterz, I., Al-Duwairi, B.: Identifying potentially useful email header features for email spam filtering. In: ICDS The Sixth International Conference on Digital Society (2012)
Awad, W.A., ELseuofi, S.M.: Machine learning methods for spam e-mail classification. Int. J. Comput. Sci. Inform. Technol. 3(1), 173–184 (2011)
Eberhardt, J.J.: Bayesian spam detection. Scholarly Horizons: University of Minnesota, Morris Journal, vol. 2, no. 1 (2015)
Freeman, D.M.: Using naive Bayes to detect spammy names in social networks. In: Proceedings of the 2013 ACM Workshop on Artificial Intelligence and Security, AISec 2013, pp. 3–12. New York (2013)
Lee, S., Jeong, I., Choi, S.: Dynamically weighted hidden Markov model for spam deobfuscation. In: IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2523–2529 (2007)
Lee, H., Ng, A.Y.: Spam deobfuscation using a hidden Markov model. In: Proceedings of the 2nd Conference on Email and Anti-Spam, Stanford University (2005)
Jaswal, V., Sood, N.: Spam detection system using hidden Markov model. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(7), 304–308 (2013)
Roy, S., Patra, A., Sau, S., Mandal, K., Kunar, S.: An efficient spam filtering techniques for email account. Am. J. Eng. Res. 2(10), 63–73 (2013)
Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theor. 13(2), 260–267 (1967)
Papoulis, A.: Probability, Random Variables, and Stochastic Processes. McGraw-Hill Series in Electrical Engineering, 3rd edn. (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Mansourbeigi, S.MH. (2019). Stochastic Methods to Find Maximum Likelihood for Spam E-mail Classification. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2019. Advances in Intelligent Systems and Computing, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-15035-8_60
Download citation
DOI: https://doi.org/10.1007/978-3-030-15035-8_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15034-1
Online ISBN: 978-3-030-15035-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)