Detection of Spams using Extended ICA & Neural Networks
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - August Issue 2013 by IJCTT Journal | ||
| Volume-4 Issue-8 | ||
| Year of Publication : 2013 | ||
| Authors :Deepinderjeet Kaur, Amandeep Kaur |
Deepinderjeet Kaur, Amandeep Kaur "Detection of Spams using Extended ICA & Neural Networks"International Journal of Computer Trends and Technology (IJCTT),V4(8):2619-2624 August Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract:- Spams are the textual context of the system which can damage the system. The basic problem is to protect the system from such type of unwanted files. To save from system form such kind of failures we design a system which can recognize the spams and can let you know on the basis of training system. The first part consists of filling the ip address or header in the text box. In the ip.txt file we put the ip addresses of those countries or region which we want to be marked as spam and on the other hand in header.txt file we put the headers of all our contacts. In the second part we detect the spam as we compare the content of the given file with the spam.txt file. In the spam.txt file we put the spam words. For detection purposes, we used ICA++ algorithm and for matching purpose, we used Neural Networks. If the 70% of the data of the given file matches with the spam words then it is declared as spam file and at the end there are comparison between PCA & ICA++, first on the basis of max error rate second on the basis of processing time third on the basis of accuracy.
References-
[1] Viruslist.com (2007), Contemporary Spammer Technologies available from: http://www.viruslist.com/en/spam/info?chapter=153350528
[2] NetworkWorld.com (2007), Spam Calculator available from: http://www.networkworld.com/spam/index.jsp
[3] SecureWorks (2007), Storm Worm DDoS Attack available from: http://www.secureworks.com/research/threats/view.html?threat=st orm-worm
[4] The TechWeb Network (2007), Dutch Botnet Suspects Ran 1.5 Million Machines available from: http://www.techweb.com/wire/security/172303160
[5] BBC News website (2007), Criminals `may overwhelm the web` available from: http://news.bbc.co.uk/2/hi/business/6298641.stm
[6] Bächer P., Holz T., Kötter M. and Wicherski G. (2007), Know your Enemy: Tracking Botnets available from: http://www.honeynet.org/papers/bots/
[7] White Papers . Spam Tutorial. . VicomSoft. http://www.spambolt.com/anti_spam_faq/ email_spam_filter.html
[8] Sivanadyan, Detecting spam using Neural Networks. http://www.cae.wisc.edu/~ece539/project/f03/sivanadyan.pdf
[9]Patrick Pantel and Dekang Lin.‘‘SpamCop.A Spam Classification & Organization Program. Proceedings of AAAI-98, Workshop on Learning for Text Categorization.
[10] Paul Graham, at the 2003 Spam Conference http://www.paulgraham.com/better.html
[11] Lindsay I Smith. A tutorial on Principal Components Analysis http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_co mponents.pdf
[12] Martin, Spam Filtering Using Neural Networks http://web.umr.edu/~bmartin/378Project/report.html
[13] The Great Spam Archive (Online spam database) http://www.annexia.org/spam/
Keywords : — Spam, Detection of Spams, ICA++, PCA, Neural Network.