Spam classification by using association rule algorithm based on segmentation

  • Abstract
  • Keywords
  • References
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  • Abstract

    Email is a most widespread and active communication technique. The major purpose behind the success of email is the vast availability, facility of utilize, and affordability. Therefore this technology has be a susceptible to malicious attacks; Email is the most frequently applied delivery technique for malware. E-mail spam is one of the major problems of the Internet today, and get financial harm to companies and individual users is uncomfortable. Spam mail can be harmful as they may include malware & links to phishing Web sites. So necessary to divide spam from mail messages to a separate folder. In this paper utilize one of datamining mechanism is association rule association rule; pattern discovered based on relationship between item-sets. The dataset utilized in proposed system is Enron dataset is divided into two parts: spam and non-spam. For extract features from dataset used Term Frequency Invers Term Frequency (TFIDF) method. For reduce dimensionality of feature space use Information Gain (IG) method.



  • Keywords

    Spam; Association Rule; Information Gain; Term Frequency Invers Term Frequency.

  • References

      [1] AakankshaSharaff, Naresh Kumar Nagwani, And Kunal Swami," Impact Of Feature Selection Technique On Email Classification",June 2015

      [2] Jon Kågström, “Improving Naive Bayesian Spam Filtering”, M.Sc. Thesis, Mid Sweden University Department For Information Technology And Media, Spring 2005

      [3] Ciphertrust, “Spam: A Security Issue”, Ciphertrust, Inc. White Paper, December 2003.

      [4] M. Basavaraju, And R. Prabhakar, “A Novel Method Of Spam Mail Detection Using Text Based Clustering Approach”, International Journal Of Computer Applications (0975 – 8887) Volume 5, No.4, August 2010.

      [5] Jiawei Han, Michelinekamber and Jian Pei," Data Mining Concepts and Techniques ", third Edition, 2012.

      [6] Karim Al-Saedi, S. Manickam, S. Ramadass, W. Al-Salihy and A. Almomani, 2013. "Research Proposal: An Intrusion Detection System Alert Reduction And Assessment Framework Based On Data Mining" Journal Of Computer Science. Volume 9, Issue 4. Pp: 421- 426. New York, Usa.

      [7] S. Divya, And T. Kumaresan, “Email Spam Classification Using Machine Learning Algorithm”, International Journal Of Innovative Research In Computer And Communication Engineering, Vol.2, Special Issue 1, March 2014.

      [8] Wang Y., Liu Y., Feng L., and Zhu X., "Novel Feature Selection Method Based On Harmony Search for Email Classification", Knowledge-Based Systems 73, 311–32, 2015.

      [9] Thuzarphyu,Nyein,"Performance Comparison Of Feature Selection Methods",Yangon Technological University,2016.

      [10] Tianda Yang, Kaiqian Et Al," Spam Filtering Using Association Rules And Naïve Bayes Classifier",Ieee,2016.

      [11] G.Kaur, R. K. Gurm, “A Survey On Classification Techniques In Internet Environment”, In International Journal Of Advance Research In Computer And Communication Engineering, Vol. 5, No. 3, Pp. 589–593, 2016.

      [12] V.Christina, S.Karpagvalli, G.Suganya,"Email Spam Filtering Using Supervised Machine Learning Techniques", 2010.

      [13] Eshabansal,PradeepKumarbhai,"A Survey Of Various Machine Learning AlgorithmsOn Email Spamming ",Irf International Conference, 8th January, ,Dravidian University, 8th January, January 2017.

      [14] Harjot Kaur*, Er. Prince Verma, "Survey on E-Mail Spam Detection Using Supervised Approach with Feature Selection", Nternational Journal of Engineering Sciences & Research Technology, April 2017.

      [15] P. Verma And D. Kumar, “Association Rule Mining Algorithm’s Variant Analysis,” In International Journal Of Computer Applicaation, Vol. 78, No. 14, Pp. 26–34, 2013.

      [16] SeongwookYoun, And Dennis Mcleod, "Spam Email ClassificationUsing An Adaptive Ontology", Journal Of Software, Vol. 2, No. 3, Pp.43-55, September 2007.




Article ID: 18486
DOI: 10.14419/ijet.v7i4.18486

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