spamming

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In 2003, et al. Jerome R. Bellegarda, showed the conventional mail filtering techniques based on unsupervised learning where the classification is done on the basis keyword matching. But if spammers change the tricks of spam mails framing than the old classifiers will than not able to give the accurate results. That is the worst part of the unsupervised learning. On the other hand, in the same paper, machine learning techniques based on supervised learning is introduced where the classifiers are regularly fed with the changing patterns of spam mails with different data sets[15]. In 2006, et al. Giorgio Fumera, focused in his work in [20] on text categorization techniques based on the machine learning and pattern recognition approaches for e-mail semantic content analysis instead of manually coded rules derived from the analysis of spam e-mails. This paper lighted the concept of content based spam filtering and anti-spam filtering which exploits the text information embedded into images sent as attachments. In 2009, et al. Ronald Bhuleskar tricked a new approach of HSF Model. It is a combinational filter model of various spam filteration techniques. Author used unsupervised and supervised techniques simultaneously in its model. It filters an incoming mail through various filters separately but all the filters should be arranged in parallel. Parallel filters used in this paper were black and white listing, content based filtering and Forging filtering. In forging, IP address of the sender is checked and then at server level validating the domain name of Email sending server with its IP address or Reverse DNS Lookup[18]. In 2010, et al. Morteza Zi Hayat showed in [19], again the supervised learning is used and promoted. In this re... ... middle of paper ... ...s and Networks, IEEE Computer Society, 2009, pp. 302-307. [19] Morteza Zi Hayat, Javad Basiri, Leila Seyedhossein, Azadeh Shakery, “Content-Based concept drift detection for email spam filtering”, 5th International Symposium on Telecommunications (IST'2010), 2010, pp. 531-536. [20] Giorgio Fumera, Ignazio Pillai, Fabio Roli, “Spam filtering based on the analysis of text information embedded into images”, in Journal of Machine Learning Research, Vol. 7, Dec.-2006,pp. 2699-2720. [21] Zhenyu Zhong, Kang Li, “Speed up statistical spam filter by approximation”, ieee transactions on computers, vol. 60, no. 1, january 2011, pp. 120-133. [22] Basheer Al-Duwairi, Ismail Khater , Omar Al-Jarrah, “Detecting image spam using image texture features”, International Journal for Information Security Research (IJISR), Volume 2, Issues 3/4, September/December 2012, pp. 344-353.

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