The week's pick
Random Articles
Reseach Article
Survey Paper on User Defined Spam Boxes using Email Filtering
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 157 - Number 6 |
| Year of Publication: 2017 |
| Authors: Rupali S. Vairagade, Nikhil Jaunjal, Varun Joshi, Advait Patil, Sudhakar Chavan |
10.5120/ijca2017912633
|
Rupali S. Vairagade, Nikhil Jaunjal, Varun Joshi, Advait Patil, Sudhakar Chavan . Survey Paper on User Defined Spam Boxes using Email Filtering. International Journal of Computer Applications. 157, 6 ( Jan 2017), 22-24. DOI=10.5120/ijca2017912633
Abstract
Spam filtering is the processing of email to build it according to various criteria. Most repeatedly this refers to the regularly processing of incoming emails, but the sentence also applies to the intervention of human perception as well as spam filtering techniques, and to receiving emails as well as those being received. Email filtering is a software inputs email. For its output, it might be path of the message through unchanged for transmission to the user's mailbox and it will redirect the message for delivery somewhere, or even start the message away. Some email filters are able to changed messages during processing. Mail filters can complete on send and receive email traffic. Send email filtering involves scanning emails from the Internet confirm to users protected by the spam filtering system or for authorized interference. Receive email filtering involves the backward - scanning email messages from local users before any potentially toxic messages can be delivered to others on the Internet. One method of outbound email filtering that is commonly used by ISP is transparent TSP client network , in which email traffic is prevent and filtered via a transparent proxy within the network. Receive email-filtering can also take place in a local server. Many organization use data should be crack and it prevention technology in their receive email servers to prevent the flow of sensitive information via email.
References
- Xiao-li, C., Pei-yu, L., Zhen-fang, Z., & Ye, Q. (2009, August). “A method of spam filtering based on weighted support vector machines.” In IT in Medicine &Education, 2009. ITIME'09. IEEE International Symposium on (Vol. 1, pp. 947-950). IEEE.
- Youn, S., & McLeod, D. (2007). “A comparative study for email classification.” In Advances and Innovations in Systems, Computing Sciences and Software Engineering (pp. 387-391). Springer Netherlands.
- Sculley, D., & Wachman, G. M. (2007, July). “Relaxed online SVMs for spam filtering.” In Proceedings of the 30th annual international ACMSIGIR conference on Research and development in information retrieval (pp. 415-422).
- Miszalska, I., Zabierowski, W., & Napieralski, A. (2007, February).”Selected Methods of Spam Filtering in Email.” In CAD Systems in Microelectronics, 2007. CADSM'07. 9th International Conference-The Experience of Designing and Applications of (pp. 507-513). IEEE.
- Sharma, S., & Arora, A. (2013). “Adaptive Approach for Spam Detection” International Journal of Computer Science Issues (IJCSI), 10(4).
- Sharma, S., & Arora, A. (2013). “Adaptive Approach for Spam Detection” International Journal of Computer Science Issues (IJCSI), 10(4).
- Xiao-li, C., Pei-yu, L., Zhen-fang, Z., & Ye, Q. (2009, August). “A method of Spam filtering based on weighted support vector machines.” In IT in Medicine & Education, 2009. ITIME'09. IEEE International Symposium on (Vol. 1, pp. 947-950). IEEE.
- Sculley, D., & Wachman, G. M. (2007, July). “Relaxed online SVMs for spam Filtering.” In Proceedings of the 30th annual international ACM SIGIR Conference on Research and development in information retrieval (pp. 415-422).
- Puniškis, D., Laurutis, R., & Dirmeikis, R. (2006). “An artificial neural nets for spam e–mail recognition.” Elektronika ir Elektrotechnika (Electronics and Electrical Engineering), 5(69), 73-76.
Index Terms
Keywords