# Savita Teli 1, Santoshkumar Biradar 2

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3 Disadvantage is it can t see any disadvantages to using Blackholes in order to detect spam, they are important as they enable blacklist to be updated with computers that are sending unwanted s Greylists A relatively new spam filtering technique, it takes the advantage of the fact that many spammers only attempt to send a batch of junk mail once. Under the greylist system, the receiving mail server initially rejects messages from unknown users and sends a failure message to the originating server. If the mail server attempts to send the message second time- a step most legitimate server will take the greylist assumes the message is not spam and let it proceed to the recipient s inbox. At this time greylist filter will add the recipient s or address to a list of allowed senders. Though greylist filter require fewer system resources than some other types of spam filters, they also delay mail delivery, which could be inconvenient when you re expecting time sensitive messages. 4.2 Content Based Filter Content Based Filter is the most commonly used group of methods to filter spam. Content filter act either on the content, the information contained in the mail body, or on the mail headers (like Subjects ) to either classify, accept or reject a message [3]. 4.3 Bayesian Filter It is considered to be a more advanced form of Content Based Filter, employ the laws of mathematical probability to determine which messages are legitimate and which are spam. Bayesian filter learn from both good and spam s, result in an adapting and efficient anti_spam approach Mathematical foundation Bayesian filter take the advantage of Bayesian theorem is P (spam/word) = [P (word/spam) P (spam)] / p (word) Theorem in the context of spam, says that the probability that an is spam, given that it has certain words in it, is equal to the probability of finding those certain words probability that any is spam, divided by the probability of finding those words in any Process Fig.2 creating Word Database for Filter Figure 2: Creating Database for Filter International Conference on Advances in Engineering & Technology 2014 (ICAET-2014) 70 Page

5 VI. CONCLUSION This paper has described some spam detection method and various problem associated with spam. From the study we conclude that we can t stop the spam but we can reduce it nicely by Bayesian method as compare to other method. References: [1] Christina V, Karpagavalli S, Suganya G, A Study on Spam Filtering Techniques, International Journal of Computer Applications ( ) Volume12-No.1, December [2] Saadat Nazirova, Survey on Spam Filtering Techniques, Scientific Reaserch-Vol. 3, No. 3, August [3] Gordon V.Cormack,David R.Cheriton, Spam Filtering: A Systematic Review, Foundation and Trends in Information Retrieval-Vol. 1, No.4(2006). [4] (GFI is Microsoft Gold certified pattern) [5] Chi-yao Tseng, Pin-Chieh Sung, and Ming-Syan, Cosdes:A Collaborative spam Detection System with a Novel Abstraction Scheme, IEEE Transactions on Knowledge and Data Engineering, Vol-23, No. 5, May [6] G, Ashokkumar, S.Dhineskumar, Spam Filtering, Department of Computer Science and Engineering, Anna University, Channai , India. [7] David Mertz, Spam filtering Techniques, IBM Developer Works. International Conference on Advances in Engineering & Technology 2014 (ICAET-2014) 72 Page

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