Design of a Machine Learning Based Predictive Analytics System for Spam Problem

• Authors: A. Yüksel , Ş. Çankaya , İ. Üncü
• Languages of publication: EN

Abstracts

EN

Spamming is the act of abusing an electronic messaging system by sending unsolicited bulk messages. Filtering of these messages is merely another line of defence and does not prevent spam messages from circulating in email systems. This problem causes users to distrust email systems, suspect even legitimate emails and leads to substantial investment in technologies to counter the spam problem. Spammers threaten users by abusing the lack of accountability and verification features of communicating entities. To contribute to the fight against spamming, a cloud-based system that analyses the email server logs and uses predictive analytics with machine learning to build trust identities that model the email messaging behavior of spamming and legitimate servers has been designed. The system constructs trust models for servers, updating them regularly to tune the models. This study proposed that this approach will not only minimize the circulation of spam in email messaging systems, but will also be a novel step in the direction of trust identities and accountability in email infrastructure.

Keywords

EN

spam; predictive analytics; machine learning; trust identities

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2017
• Volume: 132
• Issue: 3
• Pages: 500-504

Dates

published

2017-09

Contributors

author

A. Yüksel

• Süleyman Demirel University, Faculty of Engineering, Department of Computer Engineering, Isparta, Turkey

author

Ş. Çankaya

• Süleyman Demirel University, Faculty of Engineering, Department of Computer Engineering, Isparta, Turkey

author

İ. Üncü

• Süleyman Demirel University, Faculty of Technology, Department of Electrical and Electronic Engineering, Isparta, Turkey

References

• [1] A. Pathak, Y.C. Hu, Z.M. Mao, in: Proc. 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats, Berkeley (USA), 2008, p. 3
• [2] C. Kanich, C. Kreibich, K. Levchenko, B. Enright, G.M. Voelker, V. Paxsonf, S. Savage, Spamalytics: An Empirical Analysis of Spam Marketing Conversion, ACM 2008
• [3] A. Ramachandran, N. Feamster, D. Dagon, in: Proc. 2nd Conf. on Steps to Reducing Unwanted Traffic on the Internet, Berkeley (USA), Vol. 2, 2006, p. 8
• [4] E. Passerini, R. Paleari, L. Martignoni, D. Bruschi, in: Detection of Intrusions and Malware, and Vulnerability Assessment, Ed. D. Zamboni, Springer, Berlin 2008, p. 186
• [5] Y. Xie, F. Yu, R. Panigrahy, Spamming Botnet: Signatures and Characteristics, Microsoft Res., 2008
• [6] S. Shah, B.D. Noble, Softw. Pr. Exper. 37, 1515 (2007)
• [7] B. Leiba, J. Ossher, V.T. Rajan, R. Segal, M.N. Wegman, in: CEAS 2005 - 2nd Conf. on Email and Anti-Spam, 2005, Stanford University, California 2005
• [8] S. Kumar, X. Gao, I. Welch, M. Mansoori, in: 2016 IEEE 30th Int. Conf. on Advanced Information Networking and Applications (AINA), 2016, p. 973
• [9] H. Xue, F. Li, H. Seo, R. Pluretti, in: 2015 IEEE Trustcom/BigDataSE/ISPA, 2015, Vol. 1, p. 726
• [10] L. Chen, Z. Yan, W. Zhang, R. Kantola, Future Gener. Comput. Syst. 49, 77 (2015)
• [11] Y. Dai, S. Tada, T. Ban, J. Nakazato, J. Shimamura, S. Ozawa, in: Neural Information Processing, Eds. C.K. Loo, K.S. Yap, K.W. Wong, A.T.B. Jin, K. Huang, Springer Int. Publ. 2014, p. 365
• [12] Machine Learning Documentation Microsoft Azure http://azure.microsoft.com/en-us/documentation/services/machine-learning/
• [13] D. Edla, V. Reddy, V. Gondlekar, V. Gauns, Acta. Phys. Pol. A 130, 78 (2016), doi: 10.12693/APhysPolA.130.78
• [14] E. Kanca, F. Çavdar, M.M. Erşen, Acta. Phys. Pol. A 130, 365 (2016), doi: 10.12693/APhysPolA.130.365
• [15] M. Cevri, D. Üstündağ, Acta. Phys. Pol. A 130, 45 (2016), doi: 10.12693/APhysPolA.130.45
• [16] N. İyit, H. Yonar, A. Genç, Acta. Phys. Pol. A 130, 397 (2016), doi: 10.12693/APhysPolA.130.397
• [17] M. Günay, V.H. Sanchez Espinoza, A. Travleev, Acta. Phys. Pol. A 128, B-110 (2015), doi: 10.12693/APhysPolA.128.B-110
• [18] Apache, SpamAssassin http://spamassassin.apache.org

Identifiers

DOI

10.12693/APhysPolA.132.500