Using Live Spam Beater (LiSB) Framework for Spam Filtering during SMTP Transactions
Source abstract
Abstract
This study introduces the Live Spam Beater (LiSB) framework for the execution of email filtering techniques during SMTP (Simple Mail Transfer Protocol) transactions. It aims to increase the effectiveness and efficiency of existing proactive filtering mechanisms, mainly based on simple blacklists. Since it implements some proactive filtering schemes (during SMTP transaction), when an email message is classified as spam, the sender can be notified by an SMTP response code as a result of the transaction itself. The presented framework is written in Python programming language, works as an MTA (Mail Transfer Agent) server that implements an SMTP (Simple Mail Transfer Protocol) reverse proxy and allows the use of plugins to easily incorporate new filtering techniques designed to operate proactively. We also include a plugin to perform proactive content-based filtering through the analysis of words included in the body of the email message. Finally, we measured the performance of the plugin and the framework (time required for operation and accuracy) obtaining values suitable for their use during SMTP transactions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Spam and Phishing Detection · Network Security and Intrusion Detection · Internet Traffic Analysis and Secure E-voting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, Universidade de Vigo, ESEI—Escola Superior de Enxeñaría Informática, Edificio Politécnico, Campus Universitario As Lagoas S/N, 32004 Ourense, Spain
CINBIO—Biomedical Research Centre, Universidade de Vigo, Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain
SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Hospital Álvaro Cunqueiro Bloque técnico, Estrada de Clara Campoamor, 341, 36312 Vigo, Spain
Funding
Conselleria de Cultura, Educación e Universidade (Xunta de Galicia)
References (20)
Show source-provided references
1. An Open Digest-Based Technique for Spam Detection
2. Levine, J. (2019). Email Authentication for Internationalized Mail, IETF. Request for Comments Published by IETF Number 7669.10.17487/RFC8616 ↗
3. DNS-Based Email Sender Authentication Mechanisms: A Critical Review10.1016/j.cose.2009.05.002 ↗
4. A Survey of Learning-Based Techniques of Email Spam Filtering10.1007/s10462-009-9109-6 ↗
5. Spam Filtering Using Statistical Data Compression Models
6. Using Evolutionary Computation for Discovering Spam Patterns from E-Mail Samples10.1016/j.ipm.2017.12.001 ↗
7. Herrera Silva, J.A., Barona López, L.I., Valdivieso Caraguay, Á.L., and Hernández-Álvarez, M. (2019). A Survey on Situational Awareness of Ransomware Attacks—Detection and Prevention Parameters. Remote Sens., 11.10.3390/rs11101168 ↗
8. A Novel Real-Time Anti-Spam Framework10.1145/3423153 ↗
9. Chen, J., Paxson, V., and Jiang, J. (2020, January 12–14). Composition Kills: A Case Study of Email Sender Authentication. Proceedings of the USENIX Security Symposium, San Diego, CA, USA.
10. Dada, E., and Joseph, S. (2018). Random Forests Machine Learning Technique for Email Spam Filtering, University of Maiduguri.
11. A Comprehensive Survey for Intelligent Spam Email Detection10.1109/ACCESS.2019.2954791 ↗
12. Effect of Header-Based Features on Accuracy of Classifiers for Spam Email Classification
13. Bin Abd Razak, S., and Bin Mohamad, A.F. (2013, January 8–10). Identification of Spam Email Based on Information from Email Header. Proceedings of the 2013 13th International Conference on Intellient Systems Design and Applications, Salangor, Malaysia.10.1109/ISDA.2013.6920762 ↗
14. Machine Learning for Email Spam Filtering: Review, Approaches and Open Research Problems10.1016/j.heliyon.2019.e01802 ↗
15. Survey of Review Spam Detection Using Machine Learning Techniques10.1186/s40537-015-0029-9 ↗
16. Crocker, D., Hansen, T., and Kucherawy, M. (2011). Domain Keys Identified Mail (DKIM) Signatures, IETF. Request for Comments Published by IETF Number 6376.10.17487/rfc6376 ↗
17. Enhancing Representation in the Context of Multiple-Channel Spam Filtering10.1016/j.ipm.2021.102812 ↗
18. Kitterman, S. (2014). Sender Policy Framework (SPF) for Authorizing Use of Domains in Email, Version 1, IETF. Request for Comments Published by IETF Number 7208.10.17487/rfc7208 ↗
19. Khan, S.A., Iqbal, K., Mohammad, N., Akbar, R., Ali, S.S.A., and Siddiqui, A.A. (2022). A Novel Fuzzy-Logic-Based Multi-Criteria Metric for Performance Evaluation of Spam Email Detection Algorithms. Appl. Sci., 12.10.3390/app12147043 ↗
20. Wirebrush4SPAM: A Novel Framework for Improving Efficiency on Spam Filtering Services: Wirebrush4spam: A novel framework for spam filtering10.1002/spe.2135 ↗