Spam Filtering using Signed and Trust Reputation Management

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1 Spam Filtering using Signed and Trust Reputation Management G.POONKUZHALI 1, K.THIAGARAJAN 2,P.SUDHAKAR 3, R.KISHORE KUMAR 4, K.SARUKESI 5 1,4 Department of Computer Science and Engineering, Rajalakshmi Engineering College, Affiliated to Anna University- Chennai, Tamil Nadu 2 Department of Science and Humanities, KCG College of Technology Affiliated to Anna University-Chennai, Tamil Nadu 3 Vernalissystems Pvt Ltd, Chennai Hindustan Institute of Technology and Science-Chennai,Tamil Nadu INDIA Abstract: - In the revolution most of the messages contain SPAM which clogs up the inbox and is quite obnoxious. Therefore, managing a mailbox has become a big task in the faster e-world. Especially, when the user linked with social networks, user s inbox is occupied with several kinds of SPAM e- mails which lead to many problems. Deduction of spam mails has become an important issue in e-world. In this paper a mathematical approach based on signed and trust reputation management is developed to restrict the spam s through user s attitude on a particular and content relevancy of the . The results obtained by this approach are similar to the results obtained through ID3 classifier. Key-Words: - attitude, , rating, relevant, spam, trust. 1 Introduction Due to the intensive use of internet for sending messages, unsolicited commercial messages known as spam also creeps in to inbox. These are harmful and have offensive comment. Due to the low cost involved in sending s, Companies and several people send bulk messages in the form of spam [5]. Content-based classification analyzes the contents and packet of an using Bayesian networks [11, 12] or pattern matching [13]. Spam in the past contains known strings or patterns which are not necessary for the user. Unfortunately, majority of clients now render Hypertext Markup Language (HTML) based s, allowing spammers many opportunities to fool the filters. Content-based filters require never-ending tuning and adjustment in order to keep up with the spammers latest tricks. Another approach is Domain Keys Identified Mail (DKIM) [14], which associates a responsible identity with each . Allowing the receiver to confirm the sender and origin of the . Unfortunately, this system does not prevent the bot from using the identities stored on the hijacked computer and sending through the domain s relays. It does however, make it easier to identify the source of the . Adoption, as in many other cases, may prove to be the biggest hurdle for DKIM. The work in this paper is directed towards handling such messages. We use signed based approach for classifying s and directing them in spam folder or inbox of the user.this paper proposes five steps for the managing Attitude analysis, 2. Pre-processing, 3. Relevancy analysis, 4. Post-Processing, 5. Final decision making. In the process of the attitude analysis filter compares the address of the sender with the content of the address book of the receiver and analysis of the subject and based on this analysis + and signs are assigned to the . In the preprocessing phase content of the is checked. Pre-processing stage consist of stemming where all the HTML tags are removed followed by stop word removal where the words which do not form any meaning to the sentence are removed from the ISBN:

2 content. Final stage of pre-processing consists of the feature extraction where the left over content of the is tokenized. In the third phase of the spam detection, relevancy analysis is done where the left over content of the is compared with the content of the positive dictionary and the content of the negative dictionary. Based on the comparison positive and negative counts are established. In the post processing phase the positive and the negative counts are compared. If the positive count is greater than the negative count, then the left over tokens in the is entered in to the positive dictionary else the left over tokens are entered in to negative dictionary. In the final decision stage the is moved to the inbox or to the spam based on the ratings. Downsides of the existing system 1. The spam guards used in s today are generalized based on the service providers. 2. User centric approach of spam guard is not followed rather than application centric approach is followed. 3. Customizable options of a spam guard are limited. 4. Classification of normal and spam s is having more preference over management of s chosen by the user Incoming Receive in Input buffer folder Attitude Analysis Phase Pre-processing Phase Relevancy Analysis Phase Decision Making Positive Dictionary Negative Dictionary 2 Architectural Design The figure shows the architectural design of the proposed spam management system. When an arrives to the proposed system, it pass through four phases for spam deduction. They are i) Attitude analysis phase ii) Pre processing of the received e- mail iii) relevancy analysis phase and iv) Post processing to make final decision to categorize normal and spam . In Attitude analysis, users interest on the was consider based on the sender address available in his address book. In the pre-processing phase contents are proposed. In the relevancy analysis phase contents are analysed for relevancy. A Domain dictionary of words is used in the relevancy analysis phase. Based on the outcome of the previous phases, the final decision was mode. Decision making process recommends whether the received is to be placed in inbox or spam. Move to Inbox Hold Move to Spam Fig. 1 Architectural Design 2.1 Attitude Analysis Phase Attitude analysis is done on the incoming e mail received by the user based on the following attributes namely sender s id and subject. Action is determined based on the existence of sender s id and trusted subject of the along with a weighted score of 0.25 is given to each attribute. Table 1: Rating based on attitude analysis ref [3] id Subject Rating Action Exist Trusted ++ A Exist Not Trusted +- B Not Exist Trusted -+ C Not Exist Not Trusted -- D ISBN:

3 2.2 Pre-processing Content of the is extracted and pre-processed to proceed the next phase. The pre-processing phase transforms the extracted content into a structured form. In the case of text mails, stop words removal and stemming of the words are carried on. 2.3 Relevancy Analysis Phase The second phase in the process is to verify the content (body) of the for confirming the relevancy of the with the context which is preferred by the user. Each word in the preprocessed content will be compared with the positive dictionary to examine the relevancy of the content. If the words in the content of the are present in the positive dictionary then the process leads to the next stage. Otherwise, the words in the content are compared with the negative dictionary to make sure if it contains any spam prone words. If more than 50% of the word content is relevant then + sign (Positive rating) is assigned with a weighted value of 0.5 else - sign (Negative rating) is assigned with a weighted value of 0 for irrelevant content. 2.4 Post Processing and Final decision making Final decision is made based on the weighted score of the attributes of both attitude analysis phase and relevancy analysis phase. The attitude analysis holds 0.5 weightage for both id and subject trusted. Similarly, relevancy analysis phase holds 0.5 weightage for relevant content. If the weighted value is greater than 0.5 then the is moved to Inbox and the pre-processed root words which are not already exist are added to positive dictionary. If the weighted value is less than 0.5 then the is moved to spam and the pre-processed root words which are not already exist are added to negative dictionary. If the weighted value is equal to 0.5 then the is hold. The number of normal that are classified as spam and the reverse will be significantly trim down since there are a two levels of validating a in the system. Also user can classify spam and ham according to his personal interest on a particular rather than going for a generalized spam filter. Table 2. Decision made based on Attitude and Relevancy rating Attitude Relevancy Weighted value Decision made Move to inbox Move to inbox Hold Move to spam Move to inbox Hold Move to spam Move to spam 3. Experimental Results Verification ID3 algorithm is used to verify the decision made based on attitude analysis and relevance analysis. The results imply most of the Spam contains irrelevant content and not trusted subject. The result produced by the proposed algorithm is same as the result obtained by ID3 algorithm. Table 3: Dataset for classifying SPAM ID Subject Content Result Exist Trusted Relevant Inbox Exist Not Trusted Relevant Inbox Exist Trusted Irrelevant Hold Exist Not Trusted Irrelevant Spam Not Exist Trusted Relevant Inbox Not Exist Not Trusted Relevant Hold Not Exist Trusted Irrelevant Spam Not Exist Not Trusted Irrelevant Spam ISBN:

4 4 Conclusion Fig.3. Decision Tree This paper proposes signed approach for classification. Here two approaches are used for the classification of the . Here the user can also tag a as spam which is included in the preprocessing step. This paper proposes a self learning process for the classification of the . 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 for her cognitive ideas and dynamic discussions with respect to the paper s contribution.. References: [1] Bogdan Hoanca, How Good Are our Weapons in the Spam Wars?, Vol. 25, No.1,Spring,2006. [2] H. Brett Watson, Beyond Identity: Addressing Problems that Persist in an Electronic System with Reliable Sender Identification, Second International Conference on E- and Anti-Spam - IEEE & IACR,2005. [3] K. Thiagarajan, A. Raghunathan, Ponnamal Natarajan, G. Poonkuzhali and Prashant Ranjan, Weighted Graph Approach for Trust Reputation Managements, International Conference on Intelligent Systems and Technologies, Published in Proc. Of World Academy of Science and Technology- Vol 56, pp ,2009. [4] Ryota Mastumoto,Du Zhang and Meiliu Lu, Some empirical Results on Spam deduction Methods, IEEE Trans on Spam Deduction, April [5] Spam and Social technical gap IEEE Computer,Vol 37 Oct [6] Web Spam Taxonomy. Zolt an Gy ongyi, Hector Garcia-Molina. Proc., First International Workshop on Adversarial Information Retrieval on the Web (at the 14th International World Wide Web Conference), [7] Spam, Damn Spam, and Statistics. Dennis Fetterly, Mark Manasse and Marc Najork. Proc. of the Seventh International Workshop on the Web and Databases (WebDB 2004), 2004, Paris, France. [8] The EigenTrust algorithm for reputation management in P2P networks. S. Kamvar, M. Schlosser, and H.Garcia-Molina. Proc. of the Twelfth International World Wide Web Conference, [9] BadRank. Online at: [10] Sit, E., and Morris, R. Security considerations for peer-topeer distributed hash tables. In International Workshop on Peerto- Peer Systems (2002), vol of Lecture notes in computer science. [11] Grahm P. A plan for spam. In Reprinted in Paul Graham,Hackers and Painters, Big Ideas from the Computer Age, O Really, [12] Sahami M, Dumais S, Heckerman D and Hortivz E, A bayesian approach to filtering junk . In Workshop on Learning for Text Categorization - AAAI [13] Showalter, T. RFC 3028 Sieve: A MailFilteringLanguage. /html/rfc3028, [14] Allman E, Callas J, Delany M, Libbey M, Domain keys identified mail (dkim)signatures ISBN:

5 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 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. R.Kishore Kumar currently undergraduate student of Rajalakshmi Engineering College. He has presented 5 papers in conferences and published 4 research papers in international journals and 3 papers in national journals. One of his paper has been selected as the Best Paper. He is also the member of Computer Society of India. 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 more than 80 research papers in international and national conferences/journals. 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. ISBN:

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