# Fuzzy Logic for Spam Deduction

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4 Algorithm for Content Attack Factor Calculation: Step 1 :Split the bodycontent to words say W i where i 1 Step 2: Count the total number of words in Bodyand assign to Tw Step 3 : If T w > 0 then continue Step 4. Step 4 :Calculate word impact factor W f where W f = 0.5 /T w Step 5 :Perform comparison for each word Wi in Black list Step 6 :If match found then update the update W fi = - W f else W fi = W f ; where i Tw; Step 7 :Calculate Attach Factor = Step 8 : Calculate R4 = ; 5 was applied to calculate Attack Factor for containing attachment. If does not contain Attachment, then Attack Factor was set to zero. If any one of the attachment content was identified in virus list then Attack Factor was set to - 1. If none of the content was identified in virus list, then Attack Factor was set to 1. 5 result was assigned to R5. Result value of each was arrived by sum up previous rule results. R1 = R1; R2 = R1 + R2; R3 = R3 + R2 + R1; R4 = R4 + R3 + R2 + R1; R5 = R5 + R4 + R3 + R2 + R1; 4. Results and Discussion All fuzzy rules are applied over fuzzy input variables: Sender saddress, SenderIP, SubjectWords, ContentWords, Attachment for sample s namely A,B,C,D and E. Results of the each fuzzy rules are distributed in the table. The results of the can be obtained by plotting each rule result. Table 1: Results based on Fuzzy s The following graph was plotted for tabulated result where the X axis represent the s and Y axis predicts the Results. When line move towards the Positive value without any axis interception, then the identified is Ham. When line starts with negative and lies in the negative region without axis interception, then the is definitely spam. Whenever the result enters in the negative region, then the is set to spam. If it lies in the axis, then the is set to hold state. Result value Spam Deduction A-Ham C-Ham D-Hold A B C D E B-Spam E-Spam Figure 2. Graphical Representation ISBN:

5 5. Conclusion and Future Work In this proposed work, Fuzzy rules are constructed for 5 input parameters namely Sender s Address, SenderIP, SubjectWords, ContentWords and Attachment for common user to deduct the spam s. The proposed simplistic approach out performs in terms of accuracy and faster in deduction spam s than the existing approaches. In future, this approach can be extended based on user attitude. User can tighten or relax their spam levels by moving x axis towards positive / negative region. Acknowledgment The authors would like to thank Dr. Ponnammal Natarajan worked as Former Director Research, Anna University- Chennai,India and currently an Advisor, (Research and Development), Rajalakshmi Engineering College and Dr. K..Ravi, Associate Professor, Department of Mathematics, Sacred Heart College-Tirupattur, India for their intuitive ideas and fruitful discussions with respect to the paper s contribution. References: [1] Carreras, X. and Mdrquez, L., Boosting trees for anti-spare filtering, In Proc. of RANLP, [2] Cohen, W.W., Learning s that Classify ., Proceedings. of the AAAI Spring Symposium on Machine Learning in Information Access, Stanford, California,1996. [3] Cournane, A. and Hunt, R., An Analysis of the Tools Used For the Generation and Prevention of Spam, Computer and Security, Vol. 23, pp , [4] Cox, E., The Fuzzy System Handbook, Academic Press, Second Edition, [5] Daelemans, W., Z. Jakub, K. van der Sloot and A. van den Bosch, TiMBL: Tilburg Memory Based Learner, version 2.0, Reference Guide. ILK,Computational Linguistics, Tilburg University [6] Drucker, H., Wu, D., & Vapnik, V., Support vector machines for Spam categorization. IEEE-NN, Vol. 10, No.5, pp ,1999. [7] Graham, P., Better Baysian Filtering. In Proceedings of Spam Conference ml, [8] Hidalgo, J. G., Spez, M, and Sanz, E, Combining text and heuristicz for costsensitive spam filtering. In Proc. of CONL, [9] Lee, J., Spam: An escalating attack of the clones, The New York Times, [10] Mayer, C., and Eunjung-Cha, A., Making spam go splat: Sick of unsolicited , [11] businesses are fighting back, The Washington Post, [12] Norvig P. and Russell S., Artificial Intelligence A Modern Approach, Prentice Hall, New Jersey, [13] Nozaki, K., Ishibushi, H. and Tanaka, H., Trainable Fuzzy classification systems based on Fuzzy If-Then-s, Proc. IEEE, vol. 1, pp , [14] RFC 822: Standard for the Format of Arpa Internet Text Messages, [15] Sahami, M., Dumais, S., Heckerman, D. and Horvitz, E., A Bayesian Approach to Filtering Junk . In Learning for Text Categorization, AAA1 Workshop, pp , Madison Wisconsin, [16] SpamAssassin, P.Sudhakar received Bachelor of Engineering degree in Computer science from Anna University Chennai-India in 2006 and Master of Engineering degree in Computer Science from Anna University Chennai- India in He started his carrier as a Junior software programmer in Vernalis systems Pvt Ltd, Chennai India at 2008 and elevated to Associate software. He also presented various papers in National level conferences and published his research work in International Journals. G.Poonkuzhali received B.E degree in Computer Science and Engineering from University of Madras, Chennai, India, in 1998, and the M.E degree in Computer Science and Engineering from Sathyabama University, Chennai, India, in Currently she is pursuing Ph.D programme in the Department of Information and Communication ISBN:

6 Engineering at Anna University Chennai, India. She has presented and published 10 research papers in international conferences & journals and authored 5 books. She is a life member of ISTE (Indian Society for Technical Education),IAENG (International Association of Engineers), and CSI (Computer Society of India). K.Thiagarajan working as Senior Lecturer in the Department of Mathematics in KCG College of Technology - Chennai-India. He has totally 14 years of experience in teaching. He has attended and presented research articles in 33 National and International Conferences and published one national journal and 26 international journals. Currently he is working on web mining through automata and set theory. His area of specialization is coloring of graphs and DNA Computing. Dr. K. Sarukesi has a very distinguished career spanning of nearly 40 years. He has a vast teaching experience in various universities in India and abroad. He was awarded a commonwealth scholarship by the association of common wealth universities, London for doing Ph.D in UK. He completed his Ph.D from the University of Warwick U.K in the year His area of specializations is Technological Information System. He worked as expert in various foreign universities. He has executed number of consultancy projects. he has been honored and awarded commendations for his work in the field of information technology by the government of TamilNadu. He has published over 40 research papers in international conferences/journals and 40 National Conferences/journals. R.Kripa Keshav currently undergraduate student of Rajalakshmi Engineering College. He is the Member of computer society of india. He has presented one paper in national conference and won the best paper award and one paper published in international journal. ISBN:

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