AUTOSCALABILE DISTRIBUTED ANTI-SPAM SMTP SYSTEM BASED ON KUBERNETES
Abstract
Due to the increasing amount of spam email traffic, email users are in increasing danger, while email server resources are becoming overloaded. Therefore, it is necessary to protect email users, but also to prevent SMTP system overload during spam attacks. The aim of this paper is to design and implement an autoscalable distributed anti-spam SMTP system based on a Proof of work concept. The proposed solution extends SMTP protocol in order to enable the evaluation of client’s credibility using the Proof of work algorithm. In order to prevent resource overload during spam attacks, the antispam SMTP system will be implemented in a distributed environment, as a group of multiple anti-spam SMTP server instances. Kubernetes architecture will be used for system distribution, configured with the possibility of autoscaling the number of anti-spam SMTP server instances depending on the system load. The implemented system will be evaluated during a distributed spam attempt, simulated by a custom made traffic generator tool. Various performance tests will be given: (1) The proposed system’s impact on client’s behaviour and the overall amount of spam messages, (2) The performance of the undistributed anti-spam SMTP server during spam attack, in terms of resource load analysis (3) Autoscaling demonstration and evaluation of proposed distributed system’s performance during spam attack. It will be shown that the proposed solution has the possibility of reducing the amount of spam traffic, while processing tens of thousands of simultaneous SMTP client requests in a distributed environment.
Keywords
Full Text:
PDFReferences
D. J. C. Klensin, “Email Statistics Report 2021-2025,” 2021.
H. Faris, A. M. Al-Zoubi, A. A. Heidari, I. Aljarah, M. Mafarja, M. A. Hassonah, and H. Fujita, “An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks,” Information Fusion, vol. 48, pp. 67–83, 2019.
H. Hu and G. Wang, “Revisiting email spoofing attacks,” ArXiv, vol. abs/1801.00853, 2018.
T. Wu, S. Wen, Y. Xiang, and W. Zhou, “Twitter spam detection: Survey of new approaches and comparative study,” Computers & Security, vol. 76, pp. 265–284, 2018.
A. M. Al-Zoubi, H. Faris, J. Alqatawna, and M. A. Hassonah, “Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts,” Knowledge- Based Systems, vol. 153, pp. 91–104, 2018.
Z. Alom, B. Carminati, and E. Ferrari, “A deep learning model for twitter spam detection,” Online Social Networks and Media, vol. 18, 2020.
S. Kaddoura, O. Alfandi, and N. Dahmani, “A spam email detection mechanism for english language text emails using deep learning approach,” 2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 193–198, 2020.
A. Karim, S. Azam, B. Shanmugam, K. Kannoorpatti, andM. Alazab, “A comprehensive survey for intelligent spam email detection,” IEEE Access, vol. 7, pp. 168 261–168 295, 2019.
S. Hameed, T. Kloht, and X. Fu, “Identity based email sender authentication for spam mitigation,” in Eighth International conference on Digital Information Management (ICDIM 2013), 2013, pp. 14–19.
A. Schaub and D. Rossi, “Design and analysis of an improved bitmessage anti-spam mechanism,” 09 2015, pp. 1–5.
A. Biryukov and D. Khovratovich, “Equihash: Asymmetric proof-of-work based on the generalized birthday problem,” Ledger, vol. 2, 2017.
I. Bentov, C. Lee, A. Mizrahi, and M. Rosenfeld, “Proof of activity: Extending bitcoin’s proof of work via proof of stake,” IACR Cryptol. ePrint Arch., p. 452, 2014.
D. J. C. Klensin, “Simple Mail Transfer Protocol,” RFC 5321, Oct. 2008.
C. Dwork and M. Naor, “Pricing via processing or combatting junk mail,” in Proceedings of the 12th Annual International Cryptology Conference on Advances in Cryptology, ser. CRYPTO ’92. Berlin, Heidelberg: Springer-Verlag, 1992, pp. 139–147.
N. Poulton, Docker Deep Dive: Harness the full potential of your applications with Docker. Packt Publishing, 2020.
M. Chae, H. Lee, and K. Lee, “A performance comparison of linux containers and virtual machines using docker and kvm,” Cluster Comput, vol. 22, p.17651775, 2019.
K. Cochrane, J. S. Chelladhurai, and N. K. Khare, Docker Cookbook: Over 100 Practical and Insightful Recipes to Build Distributed Applications with Docker, 2nd ed. Packt Publishing, 2018.
K. Matthias and S. P. Kane, “Docker: Up and running: Shipping reliable containers in production,” 2015.
M. Luksa, Kubernetes in Action. Manning Publications, 2018.
N. Poulton and P. Joglekar, The Kubernetes Book. JJNP Consulting Limited, 2019.
N. Gavrilovic and V. Ciric, “Design and evaluation of proof of work based anti-spam solution,” in 2020 Zooming Innovation in Consumer Technologies Conference (ZINC), 2020, pp. 286–289.
Refbacks
- There are currently no refbacks.
ISSN: 0353-3670 (Print)
ISSN: 2217-5997 (Online)
COBISS.SR-ID 12826626