Open Access Open Access  Restricted Access Subscription or Fee Access

An Image Spam Classification Model Based on File Features Using Neural Networks

M. Soranamageswari, Dr. C. Meena

Abstract


Spam is an unauthorized intrusion into a virtual space, which caused serious economy loss and social issues. Recently, Spammers have spreading new kind of email spamming method called image spamming, which uses simple image processing technologies like varied borders or backgrounds, randomly varied spacing or margins, and adding artifacts to the images. Priceless effort, time, and money of the users and organizations are wasted in handling them. Because of the recent upsurge in image spam, the proposed system is developed to classify image spam based on file features of an image, rather than text contents by using Back propagation neural networks, which classify the incoming image as a spam or ham. The experimental result show the system correctly classifies 95% of spam images with minimum false positives.

Keywords


Back propagation, Image Spam, Machine learning and Spam filtering.

Full Text:

PDF

References


A.Bourzerdoum, A.Havstand, A.Beghadi “Image Quality Assessment using a Neural Network Approach”, IEEE, 2004, pp-330-333.

Brain Whitworth, Ellizabeth Whitworth, “Spam and the social technical gap”, IEEE computer society, 2004,pp 38- 45.

Ching-Tung Wu, Kwang-Ting Cheng, Qiang Zhu and Yi-Leh Wu, “Using Visual Features for Anti-spam Filtering ”, Proceedings of the IEEE International Conference on Image Processing 2005.

D.Streitfeld, “opening Pandora’s in-box,” Los angels Times,May 2003.available online at http://www.latimes.com/technology/la-fi-spam11may11001420,1,4347344, print.story?coll=la-mininav-technology.

Deepack p, Sandeep Parameswaran, “Spam Filtering Using Spam Mail Communities” Proceedings of the Symposium on Applications and the Internet, IEEE.2005.

Mark Dredze, Reuven Gevaryahu, Ari Elias-Bachrach, “Learning Fast Classifiers for Image Spam”, Proceedings of the fourth conference on Email and Anti-Spam, 2007.

Masahiro Uemura,Toshihiro Tabata,” Design and Evaluation of a Bayesian-filter-based Image Spam Filtering Method” IEEE Computer Society,2008,pp-46-51.

Md.Rafiqul Islam, Wanlei Zhou and Morshed U.Choudhury, “Dynamic Feature Selection for Spam Filtering Using Support Vector Machine” IEEE/ ACIS (ICIS) ,2007.

Mikko Siponen, Carl Stucke, “Effective Anti-spam Strategies in Companies: An International Study” IEEE,2006 , pp 1-10.

Minoru Sasaki, Hiroyuki Shinnou, “Spam Detection Using Text Clustering”, IEEE Proceedings of the International Conference on Cyber worlds , 2005.

Sven Krasser, Yuchun Tang, Jeremy Gould, Dmitri Alperovitch, Paul Judge “Identifying Image Spam on Header and File Properties using C4.5 Decision Trees and Support Vector machine Learning” IEEE,2007,pp-255-260.

T.Fawcett, ” In vivo spam filtering: A Challenge problem for data mining,” KDD Explorations, vol. 5,no.2,2003,pp.203-231.

Vikas P. Deshpande, Robert F. Erbacher, and Chris Harris, “An Evaluation of Naïve Bayesian Anti-Spam Filtering Techniques “, Proceedings of the IEEE Workshop on Information Assurance,2007, pp 333-340.

Yanhui Guo, Yaolong Zhang, Jianji Liu, Cong Wang, “Research on the Comprehensive Anti-Spam Filter”, IEEE, 2006, pp 1069-1074.

Zhe Wang, William Josephson, Qin Lv, Moses Charikar, Kai Li, “Filtering Image Spam With Near-Duplicate Detection”

Yan Gao, Ming Yang, Xiaonan Zhao, Bryyan Pardo, Ying Wu, Thrasyvoulos N. pappas, alok Choudhary, “ Image Spam hunter “,IEEE, 2008, pp 1765-1768.


Refbacks

  • There are currently no refbacks.