Facebook Spam and Spam Filter Using Artificial Neural Networks

Spam is any unwanted electronic message or material
in any form posted too many people. As the world is growing as
global world, social networking sites play an important role in
making world global providing people from different parts of the
world a platform to meet and express their views. Among different
social networking sites Facebook become the leading one. With
increase in usage different users start abusive use of Facebook by
posting or creating ways to post spam. This paper highlights the
potential spam types nowadays Facebook users’ faces. This paper
also provide the reason how user become victim to spam attack. A
methodology is proposed in the end discusses how to handle different
types of spam.





References:
[1] V.N. Vapnik, H. Druck, D. Wu, "Support Vector Machines for Spam
Categorization", IEEE Transactions On Neural Networks, vol. 10 no.5 ,
pp. 1048-1054, Sep 1999.
[2] L. Lazzari, M. Mari, A. Poggi, "A collaborative and multi agent
approach to e-mail filtering", IEEE/WIC/ACM International Conference
on Intelligent Agent Technology (IAT’05), pp. 238-241, 2005
[3] G. K. Tak and S. Tapaswi “Query based approach towards spam attacks
using artificial neural network” International Journal of Artificial Intelligence & Applications (IJAIA), vol.1, no.4, October 2010 DOI :
10.5121/ijaia.2010.1407 82
[4] A. Ho, A. Maiga and E. Aïmeur “Privacy Protection Issues in Social
Networking Sites” IEEE, 2009
[5] Reza Ariaeinejad and AlirezaSadeghian “Spam Detection System: A
New Approach Based on Interval Type-2 Fuzzy Sets” IEEE CCECE ,
Canada,2011
[6] F. Ahmed and M. Abulaish “An MCL-Based pproach for Spam Profile
Detection in Online Social Networks”IEEE 11th International
Conference on Trust, Security and Privacy in Computing and
Communications 2012
[7] S. Dhanaraj and Dr. V. Karthikeyani “A Study on E-mail Image Spam
Filtering Techniques” Proceedings of the 2013 International Conference
on Pattern Recognition, nformatics and Mobile Engineering (PRIME),
pp. 21-22,February 2013.
[8] A. Nosseir, K. Nagati and I. Taj-Eddin “Intelligent Word-Based Spam
Filter Detection Using Multi-Neural Networks” IJCSI International
Journal of Computer Science Issues, Vol. 10, Issue 2, No 1, March
2013M. Young, The Techincal Writers Handbook.Mill Valley, CA:
University Science, 1989.
[9] A. Kumar, S. K. Gupta, A. K. Rai and S. Sinha “Social Networking Sites
and Their Security Issues” International Journal of Scientific and
Research Publications, vol. 3, no 4, April 2013
[10] M. Vanetti, E. Binaghi, E. Ferrari, B. Carminati, and M. Carullo “A
System to Filter Unwanted Messages from OSN User Walls” IEEE
Transactions on Knowledge and Data Engineering, vol. 25, no. 2,
february 2013
[11] DolvaraGunatilaka “A Survey of Privacy and Security Issues in Social
Networks”www.cse.wustl.edu/~jain/cse571-11/ftp/social/
[12] L. Bilge, T. Strufe, D. Balzarotti, and E. Kirda, “All your contacts are
belong to us: automated identity theft attacks on social networks,” in
Proceedings of the 18th International Co