The week's pick
Random Articles
Reseach Article
A Novel Technique of Email Classification for Spam Detection
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 5 - Number 10 |
| Year of Publication: 2013 |
| Authors: Vinod Patidar, Divakar Singh, Anju Singh |
10.5120/ijais13-450976
|
Vinod Patidar, Divakar Singh, Anju Singh . A Novel Technique of Email Classification for Spam Detection. International Journal of Applied Information Systems. 5, 10 ( August 2013), 15-19. DOI=10.5120/ijais13-450976
Abstract
Email spam is one of the unsolved problems of the today's Internet, annoying individual users and bringing financial damage to companies. Among the approaches developed to stop spam emails, filtering is a popular and important one. Common uses for email filters include organizing incoming email and computer viruses and removal of spam. As spammers periodically find new ways to bypass spam filters and distribute spam messages, researchers need to stay on the forefront of this problem to help reduce the amount of spam messages. Currently spam emails occupy more than 70% of all email traffic. The negative effect spam has on companies is greatly related to financial aspects and the productivity of employees in the workplace. In this paper, we propose the new approach to classify spam emails using support vector machine. It found that the SVM outperformed than other classifiers.
References
- Aladdin Knowledge Systems, Anti-spam white paper, Retrieved December 28, 2011.
- F. Smadja, H. Tumblin, "Automatic spam detection as atext classification task", in: Proc. of Workshop on Operational Text Classification Systems, 2002.
- A. Hassanien, H. Al-Qaheri, "Machine Learning in Spam Management", IEEE TRANS. , VOL. X, NO. X, FEB. 2009
- P. Cunningham, N. Nowlan, "A Case-Based Approach to Spam Filtering that Can Track Concept Drift", [Online]
- F . Roli, G. Fumera, "The emerging role of visual pattern recognition in spam filtering: challenge and opportunity for IAPR researchers".
- I. Androutsopoulos, J. Koutsias, "An evaluation of naïve bayesian anti-spam filtering". In Proceedings of the Workshop on Machine Learning in the New Information Age, 11th European Conference on Machine Learning (ECML 2000), pages 9–17, Barcelona, Spain, 2000.
- I. Androutsopoulos, G. Paliouras, "Learning to filter spam E-mail: A comparison of a naïve bayesian and a memory based approach". In Proceedings of the Workshop on Machine Learning and Textual Information Access, 4th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2000), pages 1– 13, Lyon, France, 2000.
- J. Hidalgo, "Evaluating cost-sensitive unsolicited bulk email categorization". In Proceedings of SAC-02, 17th ACM Symposium on Applied Computing, pages 615–620, Madrid, ES, 2002.
- K. Schneider, "A comparison of event models for naïve bayes anti-spam e-mail filtering". In Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics, 2003.
- I. Witten, E. Frank, "Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations". Morgan Kaufmann, 2000.
- S. Amari, S. Wu, "Improving support vector machine classifiers by modifying kernel functions". Neural Networks, 12, 783– 789. (1999).
- C. Miller, "Neural Network-based Antispam Heuristics" , Symantec Enterprise Security (2011), www. symantec. com Retrieved December 28, 2011
- Du Toit, J. V. , de Waal, D. A. , 2010. Spam Detection using Generalized Additive Neural Networks. SATNAC 2010 Conference Proceedings.
- [Miller, Chris. "Neural Network-based Antispam Heuristics. " Symantec Enterprise Security. White paper (2006).
Index Terms
Keywords