Awareness Based Approach against E-Mail Attacks
E-mail plays a very important role in modern day communication. It is helpful for personal as well as business correspondences between people, or organizations. The major features of E-mail, which make it a convenient mode of communication, are its speed, efficiency, storage options and search facilities. Due to the high popularity of E-mails, it forms the preferred medium for a large number of web attacks. Spammers usually send spams and phishers send phishing URLs via E-mail. Of the large number of techniques that have already been proposed for the detection of several types of such attacks, quite a few of them provide good results but with higher false positives. In this paper, we are proposing a novel technique, which not only identifies spam but also scam mails, phishing, advertisements, etc. This technique utilizes some intelligence on the part of users, apart from keywords parsing, knowledge base, and token separation methods to detect various E-mail attacks. Implementation of the proposed methodology can help protect E-mail users from a wide range of unwanted E-mails, with increased efficiency and highly reduced number of false positives.
KeywordsPhishing Scam Spams Social networking Subscription
Unable to display preview. Download preview PDF.
- 2.Tak, G.K., Tapaswi, S.: Query Based Approach towards Spam Attacks using Artificial Neural Networks. International Journal of Artificial Intelligence & Applications, IJAIA (2010) ISSN: 09762191, EISSN: 0975900X, Academy & Industry Research Collaboration CenterGoogle Scholar
- 3.Saraubon, K., Limthanmaphon, B.: Fast Effective Botnet Spam Detection. In: 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology, ICCIT, pp. 1066–1070 (2009)Google Scholar
- 4.Tak, G.K., Kakkar, A.: Demand based approach to control data load on email servers. In: Tiwari, M.D., Tripathi, R.C., Agrawal, A. (eds.) Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia (IITM 2010), pp. 266–270. ACM, New York (2010), http://doi.acm.org/10.1145/1963564.1963611, doi:10.1145/1963564.1963611CrossRefGoogle Scholar
- 6.Naramore, E., Gerner, J., Scouarnec, Y.L., Stolz, J., Glass, M.K.: Beginning PHP5, Apache and MySQL Web Development ISBN: 9780764579660 Google Scholar
- 7.Yang, Y., Elfayoumy, S.: Anti-spam filtering using neural networks and Bayesian classifiers. In: Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Jacksonville, FL, USA (2007)Google Scholar
- 8.Pantel, P., Spamcop, D.L.: A spam classification and organization program. Learning for Text Categorization, Papers from the 2006 Workshop, Madison, Wisconsin, AAAI Technical Report (2006)Google Scholar
- 9.Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
- 12.Chiu, Y.-F., Chen, C.-M., Jeng, B., Lin, H.-C.: An Alliance-based Anti-Spam Approach. In: Third International Conference on Natural Computation (ICNC 2007). IEEE (2007)Google Scholar
- 13.De Capitani, D., Damiani, E., De Vimercati, S., Capitani, P., Samarati, P.: An Open Digest-Based Technique for Spam Detection. In: Proceedings of International Workshop on Security in Parallel and Distributed Systems (2004)Google Scholar
- 14.O’Donnell, A.J., Mankowski, W., Abrahamson, J.: Using E-mail Social Network Analysis for Detecting unauthorized accounts. In: Third Conference on Email and Anti-Spam, Mountain View, CA (July 2006)Google Scholar
- 16.Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: Bayesian approach to filtering junk E-mail. Learning for Text Categorization, Papers from the 1998 Workshop, Madison, Wisconsin (1998)Google Scholar
- 17.Jindal, N., Liu, B.: Analyzing and Detecting Review Spam. In: Seventh IEEE International Conference on Data Mining (ICDM), IEEE (2007)Google Scholar