Publication Cover
Cybernetics and Systems
An International Journal
Volume 47, 2016 - Issue 6
112
Views
2
CrossRef citations to date
0
Altmetric
Original Articles

An Evidential Spam-Filtering Framework

, , , &

References

  • Androutsopoulos, I., J. Koutsias, K. V. Chandrinos, G. Paliouras, and C. D. Spyropoulos. An evaluation of naive Bayesian anti-spam filtering. Paper presented at the 11th European Conference on Machine Learning, Barcelona, Spain.
  • Androutsopoulos, I., J. Koutsias, K. V. Chandrinos, and C. D. Spyropoulos. 2000. An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages. Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval 160–167. New York: ACM.
  • Carpinter, J., and R. Hunt. 2006. Tightening the net: A review of current and next generation spam filtering tools. Computers & Security 25 (8):566–578.
  • Chen, S., Y. Deng, and J. Wu. 2013. Fuzzy sensor fusion based on evidence theory and its application. Applied Artificial Intelligence 27 (3):235–248.
  • Chirita, P.-A., J. Diederich, and W. Nejdl. 2005. Mailrank: Using ranking for spam detection. In Proceedings of the 14th ACM international conference on information and knowledge management, 373–380. New York, NY: ACM.
  • Cui, B., A. Mondal, J. Shen, G. Cong, and K.-L. Tan. 2005. On effective e-mail classification via neural networks. In Database and expert systems applications. Copenhagen, Denmark: Springer.
  • Dempster, A. P. 1967. Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics 38 (2):325–339.
  • Deng, X., Q. Liu, Y. Hu, and Y. Deng. 2013. Topper: Topology prediction of transmembrane protein based on evidential reasoning. The Scientific World Journal 2013:123731.
  • Domingos, P., and M. Pazzani. 1997. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29 (2–3):103–130.
  • Drucker, H., D. Wu, and V. N. Vapnik. 1999. Support vector machines for spam categorization. Neural Networks, IEEE Transactions on 10 (5):1048–1054.
  • Edelson, E. 2003. The 419 scam: Information warfare on the spam front and a proposal for local filtering. Computers & Security 22 (5):392–401.
  • Gómez Hidalgo, J. M., M. M. López, and E. P. Sanz. 2000. Combining text and heuristics for cost-sensitive spam filtering. In Proceedings of the 2nd workshop on learning language in logic and the 4th conference on computational natural language learning, vol 7, 99–102. Association for Computational Linguistics.
  • González-Talaván, G. 2006. A simple, configurable smtp anti-spam filter: Greylists. Computers & Security 25 (3):229–236.
  • Gordillo, J., and E. Conde. 2007. An hmm for detecting spam mail. Expert Systems with Applications 33 (3):667–682.
  • Guzella, T. S., and W. M. Caminhas. 2009. A review of machine learning approaches to spam filtering. Expert Systems with Applications 36 (7):10206–10222.
  • Herzberg, A. 2009. DNS-based email sender authentication mechanisms: A critical review. Computers & Security 28 (8):731–742.
  • Hu, Y., J. Du, X. Zhang, X. Hao, E. Ngai, M. Fan, and M. Liu. 2012. An integrative framework for intelligent software project risk planning. Decision Support Systems 55 (4):927–937.
  • Hu, Y., C. Guo, E. Ngai, M. Liu, and S. Chen. 2010. A scalable intelligent non-content-based spam-filtering framework. Expert Systems with Applications 37 (12):8557–8565.
  • Hu, Y., X. Zhang, E. Ngai, R. Cai, and M. Liu. 2012. Software project risk analysis using Bayesian networks with causality constraints. Decision Support Systems 56 (2013):439–449. doi:10.1016/j.dss.2012.11.001.
  • Jung, J., and E. Sit. 2004. An empirical study of spam traffic and the use of dns black lists. In Proceedings of the 4th ACM SIGCOMM conference on Internet Measurement. AC 370–375. New York, NY: ACM.
  • Kang, B., Y. Deng, R. Sadiq, and S. Mahadevan. 2012. Evidential cognitive maps. Knowledge-Based Systems 35:77–86.
  • Klensin, J. 2008. Simple mail transfer protocol. http://www.rfc-editor.org/rfc/pdfrfc/rfc5321.txt.pdf (accessed June 20, 2016).
  • Kołcz, A., A. Chowdhury, and J. Alspector. 2004. The impact of feature selection on signature-driven spam detection. In Proceedings of the 1st conference on email and anti-spam (CEAS-2004). Mountain View, CA: CEAS.
  • Kong, G., D.-L. Xu, R. Body, J.-B. Yang, K. Mackway-Jones, and S. Carley. 2012. A belief rule-based decision support system for clinical risk assessment of cardiac chest pain. European Journal of Operational Research 219 (3):564–573.
  • Langley, P., W. Iba, and K. Thompson. 1992. An analysis of Bayesian classifiers. AAAI 90:223–228.
  • Pakdaman Naeini, M., B. Moshiri, B. Nadjar Araabi, and M. Sadeghi. 2013. Learning by abstraction: Hierarchical classification model using evidential theoretic approach and Bayesian ensemble model. Neurocomputing 130 (3):73–82. doi:10.1016/j.neucom.2012.03.041.
  • Postel, J. 1982. Simple mail transfer protocol. https://tools.ietf.org/pdf/rfc821.pdf (accessed June 20, 2016).
  • Ramachandran, A., N. Feamster, and S. Vempala. 2007. Filtering spam with behavioral blacklisting. In Proceedings of the 14th ACM conference on computer and communications security, 342–351. New York, NY: ACM.
  • Saito, T. 2005. Anti-spam system: Another way of preventing spam. In Proceedings of the sixteenth international workshop on database and expert systems applications, 57–61. IEEE.
  • Shafer, G. 1976. A mathematical theory of evidence, Vol. 1. Princeton, NJ: Princeton University Press.
  • Smets, P., and R. Kennes. 1994. The transferable belief model. Artificial Intelligence 66 (2):191–234.
  • Sommer, R., and V. Paxson. 2010. Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE Symposium on Security and Privacy (SP), 305–316. IEEE.
  • Stern, H. 2008. A survey of modern spam tools. Paper presented at CEAS 2008 – The Fifth Conference on Email and Anti-Spam, Mountain View, CA, August 21–22.
  • Su, X., Y. Deng, S. Mahadevan, and Q. Bao. 2012. An improved method for risk evaluation in failure modes and effects analysis of aircraft engine rotor blades. Engineering Failure Analysis 26 (12):164–174.
  • Van Polen, M. G., G. C. Moura, and A. Pras. 2011. Finding and analyzing evil cities on the internet. In Managing the dynamics of networks and services, 38–48. Springer.
  • Vorakulpipat, C., V. Visoottiviseth, and S. Siwamogsatham. 2012. Polite sender: A resource-saving spam email countermeasure based on sender responsibilities and recipient justifications. Computers & Security 31 (3):286–298.
  • Wang, C.-C., and S.-Y. Chen. 2007. Using header session messages to anti-spamming. Computers & Security 26 (5):381–390.
  • Wei, D., X. Deng, X. Zhang, Y. Deng, and S. Mahadevan. 2013. Identifying influential nodes in weighted networks based on evidence theory. Physica A: Statistical Mechanics and its Applications 392 (10):2564–2575.
  • Yang, J.-B., and D.-L. Xu. 2002. On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 32 (3):289–304.
  • Yoon, J. W., H. Kim, and J. H. Huh. 2010. Hybrid spam filtering for mobile communication. Computers & Security 29 (4):446–459.
  • Youn, S., and D. McLeod. 2007. A comparative study for email classification. In Advances and innovations in systems, computing sciences and software engineering, 387–391. Springer.
  • Zhang, Y., X. Deng, D. Wei, and Y. Deng. 2012. Assessment of e-commerce security using ahp and evidential reasoning. Expert Systems with Applications 39 (3):3611–3623.
  • Zhou, Z.-J., C.-H. Hu, D.-L. Xu, J.-B. Yang, and D.-H. Zhou. 2010. New model for system behavior prediction based on belief rule based systems. Information Sciences 180 (24):4834–4864.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.