# Software Engineering-Based Design for a Bayesian Spam Filter

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3 contains all message addresses that are rejected by the user. When the user receives a spam message, he can add the address of the sender to his blacklist. 4.2 Bayesian Algorithm 1) Split in tokens. i. Need number of messages for spam and legitimate. ii. Need frequency of each word for each type. 2) Calculate probabilities i. P(legitimate) = word frequency /number of legitimate messages. ii. P(spam) = word frequency/ number of spam messages. 3) Calculate likelihood of being spam (spamicity) using a special form of Bayes Rule where likelihood = a/(a + b), where a is the probability of a legitimate word and b is the probability of spam word. 4) Choose tokens whose combine probability is farthest from 0.5 either way. This is because the farther it is from 0.5 (neutral), with more certainty we can say it belongs to either strategy. i. Do this for n numbers for instance choose to have 15 extremes. ii. Combine their probability to get a figure for message using Bayes Rule. In basic terms, Baye s Rule determine the probability of an event occurring based on the probabilities of two or more independent evidentiary events. For three evidentiary events a, b, and c, the probability is equal to [10]: iii. a b c a b c ( 1 a ) * ( 1 b ) * ( 1 c ) (1) In this fashion, the rule can be expanded to accommodate any number of evidentiary events. If the end result is closer to 1.0, then the message is classified as spam, and if it is closer to 0.0, the message is classified as legitimate. The cutoff range that has been specified for spam is that it should be greater than 0.85, but spam results are above Spam Blocking System Design Data flow diagrams are developed to give the details of constructing the whole spam blocking system. These data flow diagrams are constructed according to the waterfall model using the ASCENT software [11]. The system design is divided into 6 levels, which describe the operations (processes) involved and how data flow across the system. Figure 1 depicts the basic system level (level 0). Fig.1. System level 0. When a message enters the system, a decision has to be made by the spam blocking system where to put the message. If the message is spam, it will be submitted to the bulk box, otherwise (i.e., legitimate message) it is submitted to the inbox. Re-enter direction means that a spam message has been wrongly treated as legitimate, which causes the spam message to enter the inbox as the blocking system could not discern it correctly. However, it is up to the user to set this message as a spam and add it to the training corpus. So, for the next time if this message or a similar one enters the system, it will be recognized and added to the bulk box. A star symbol has been used in figure 1 and next figures to indicate that the designated block comprises more details to be exploded in the next level. Figure 2 illustrates level one in the system data flow diagram. This level has three processes: Mail Server: accepts the message from the client and delivers it to another client according to the address. Client: receives the message from the mail server and decides whether it is a spam to send to the bulk box or it is a legitimate to send to the inbox. 85

4 SQL Server: this process contains the database, which the client relies on to make the decision concerning the received message. So, by this process the database is read, updated, and changed. Fig.2. Level 1. Level 2 which is depicted in figure 3 is an exploding of the client process that contains two processes: Mail User Agent (MUA): sends and receives messages from the mail server, and it is responsible for providing an interface for users to manage their mail. This management typically includes viewing messages, managing mail folders, and composing new s, as well as replying to a message and sending an existing message to other users. Filtering Program: checks the received message from the MUA whether it is a spam or ham. Thereafter the SQL server database is updated. Fig.3. Level 2. Level three, which is exploding of the MUA is illustrated in figure 4. It is comprised of two processes: Fig.4. Level 3. 86

5 POP3: provides a protocol for MUA to download the user's mailbox from a remote server. SMTP: implements the communication protocol between two machines that is a client and a server. In its simplest form, a message is sent from a user on one machine to a mailbox in another machine. Figure 5 depicts level four, which is the exploding of white list and blacklist checking that contains two processes: Whitelist Checking: in this process the message ID is compared with the one stored in the SQL server, which contains the addresses of the IDs of allowed users. If a match occurs, the SQL server database will be updated and the message is added to the inbox. Otherwise, in case of no match, the ID will go through another checking which is the black list. Blacklist Checking: this process makes a comparison between the messages ID that is not found in the white list table in the SQL server database with the one that contains all the addresses that are not accepted by the user. If a match occurs, the SQL server database will be updated and the message is added to the bulk box. Otherwise, the message enters to the reduction message process. Fig.5. Level 4. Figure 6, which constitutes level five, is the exploding of filtering program that contains four processes, which are: Black List & white List Checking: this process is mentioned earlier in level four. Bayesian Filter: this process is the main part in the blocking system. It does several activities: accepts the message from the reduction process, reads the database from the SQL server to make its decision for spam or legitimate, updates the database for the existence words in the message, and changes the database if a false positive Move to Spam: sometimes the message enters the inbox as legitimate, but it is really misclassified by the filtering, i.e., a false positive case has encountered. 87

6 TP R TP FN (3) When filtering spam, it is worth noting that misclassifying a legitimate mail as spam is much more severe than letting a spam message pass the filter. Letting a spam go through the filter generally does no harm while blocking an important personal mail as spam can be a real disaster. The usual precision/recall measures tell little about a filter s performance when false positive and false negative are weighted differently. To introduce some cost sensitive evaluation measures that assign false positive a higher cost than false negative, a weighted accuracy (WAcc) and weighted error rate (Werr =1-Wacc ) measures specially tailored for this scenario can be used. Wacc and Werr was introduced by Androutsopoulos et al. [13] as: n L L n S WAcc NL NS n L S n S WErr NL NS S L (4) (5) Fig.6. Level 5. In this case the user could decide that such a message will be considered as a spam in future classifications by activating the re-enter the message to the filter again. So, the database in the SQL server will be updated accordingly. 6. Performance Measures The performance measures used in this paper are introduced. Let S and L stand for spam and legitimate message, respectively. n L L, n S S denote the numbers of legitimate and spam messages correctly classified by the system i.e., true positive (TP) and true negative (TN) respectively. n L S represents the number of legitimate messages misclassified as spam i.e., false positive (FP), and n S L is the number of spam messages wrongly treated as legitimate i.e., false negative (FN). Then spam precision (P), and spam recall (R) are defined as follows [12]: where NL is the total number of legitimate messages, and NS denotes the total number of spams. WAcc treats each legitimate message as if it were λ messages: when false positive occurs, it is counted as λ errors; and when it is classified correctly, this counts as λ successes. The higher λ is, the more cost is penalized on false positives. Androutsopoulos et al. [11] also introduced three different values of λ: λ = 1, 9, and 999. When λ is set to 1, spam and legitimate mails are weighted equally; when λ is set to 9, a false positive is penalized nine times more than a false negative; for the setting of λ = 999, more penalties are put on false positive: misclassification a legitimate mail is as bad as letting 999 spam messages pass the filter. Such a high value of λ is suitable for scenarios where messages marked as spam are deleted directly. In practice, when λ is assigned a high value (such as λ = 999), WAcc can be so high that it tends to be easily misinterpreted. To avoid this problem, it is better to compare the weighted accuracy and error rate to a simplistic baseline i.e., legitimate messages are never blocked and spams can always pass the filter. P TP...(2) TP FP 88

7 7. System Evaluation To examine the performance of the proposed spam blocking system, a testing corpus of 800 e- mails; 400 for spam and 400 for ham s has been used. In order to evaluate the effect of Whitelist, Blacklist, and the Bayesian filter that are used for detecting spam, each layer is tested separately as shown in figure (7). the other two methods. This is because the Bayesian filter tests the message body rather than the addresses. Figure (8) reveals that the spam detection percentage increases with the increasing number of incoming s. This is because the statistical filter would learn more with more various features that arrive with the tested s. Fig.7. Spam Filter Detection Versus Layer. The results obtained are summarized as follows: Spam Detected by Bayesian Filter: the system is in a state of testing and this means that the system has already been trained. For that reason the detection percentage starts from a good percentage, which is 82%. Thereafter, as more learning is carried out, spam detection could approach 100% percentages. Spam detected by Blacklist: this method depends on the addresses of spammers that are stored in the Blacklist table. Initially it starts from zero and as much as the new s are entered as much as the detection increases. That is because when the new is detected as spam its address is automatically saved in a Blacklist table. Legitimate detected by Whitelist: this method depends on the s headers that are addresses of non-spammers. The detection line starts from 10% and it vibrates around this percentage. This is because of that the new e- mails entered are from persons that were initially entered in the whitelist. It may be concluded that the Bayesian filter had detected more Spam percentages rather than Fig.8. Spam Detected by the Blocking System. Figure (9) examines the effect of four measures TN, TP, FN, and FP on the tested corpus. It reveals that TN and TP increase as more messages are tested whereas FN and FP decrease as the filter has learned more. Fig.9. Percentages of Spam &Legitimate Classification. The recent results of the filter have been used to construct table (1) and figure (10) to summarize the classification results. 89

8 Table 1, Classified s by the Proposed Filter Classification TN L L TP S S FN S L FP L S number Fig.10. Percentages of Filter Classification. Table (2) shows the results of classification with three values of. As can be seen from the table WErr is acceptable low, and it is noticed that with increasing value the WErr decreases. That means the filter has a good low cost resulted from L S error type. Also as can be observed that the smallest value of has TCR 9.3, which indicates the high performance of the filter True Negative (TN) True Positive (TP) False Negative (FN) 0.04 False Positive (FP) Table 2, Performance Evaluation Results of the Proposed Filter with Three Values of Description No. WAcc WErr SP SR TCR s evaluation performance Conclusions Based on the research undertaken, the analysis and design processes of the filter and the implementation of it, several key results have been identified: The proposed filter is an aggregation of whitelist, blacklist in addition to the Bayesian filter. This has been contributed in increasing filter efficiency, decreasing false positive, and reduces classification time. Bayesian filtering includes the elements of changeability. That is it is constantly selfupdating which makes it very difficult for spammers to circumvent. The Bayesian method is multilingual. So, it is efficiently adapted in this work to manage English, Arabic, or mixed linguistic messages. The proposed solution is a client side-filtering approach that adds convenience to the user. This allows the users to custom the filter based on their interests and needs. A number of performance measurements have been carried out. The results ensure performance of the filter and its efficiency. 9. References [1] Lorenzo Lazzari, Marco Mari and Agostino Poggi, CAFÉ Collaborative Agents for Filtering s, Proceedings of the 14 th IEEE International Workshops on Enabling Technologies (WETICE 05), [2] Xiao-Lin Wang and Ian Cloete, Learning to Classify A Survey, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, pp , August [3] Yang Li, Binxing Fang, Li Guo and Shen Wang, Research of a Novel Anti-Spam Technique Based on Usere s Feedbach and Improved Naïve Bayesian Approach,

9 [4] Calto Pu, Steve Webb, Oleg Kolesnikov, Wenke Lee, and Richard Lipton, Towards the Integration of Diverse Spam filtering Techniques, Proceedings of the 22 nd International Conference on Data Engineering Workshops (ICDW"06), [5] Takamichi Saito, "Anti-Spam System: Another Way of Preventing Spam", Proceeding of the 16 th International Workshop on Database Systems Applications (DEXA"05), [6] Paul Heymann, Georgia Koutrika, and Hector Garcia-Molina, "Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges", IEEE Internet Computing, November [7] Behrouz A. Forouzan, Data Communication and Networking, McGraw-Hill, [8] Fred Halsall, Computer Networking and the Internet, Addison-Wesley, [9] Kevin Johnson, Internet Protocols, Developers Guide, Addison-Wesley Longman Inc., [10] A. McCallum and K. Nigam, A Comparison of Event Models for Naïve Bayes Text Classification, Journal on Machine Learning Research 3, pp , [11] Yeates D., and Cadle J., Project Management for Information Systems, 2 nd edition London: Finintial Times Management. [12] Wen-Feng Hsiao, Te-Ming Chang, and Guo-Hsin Hu A Cluster-based Approach to Filtering Spam under Skewed Class Distributions, Proceedings of the 40 th Hawaii International Conference on System Science, pp.1-7, [13] I. Androutspoulos, G.Sakkis, C.D. Spyropoulos, and P. Stamatopoulos, "Learning to Filter Spam s: A comparison of a Naïve Bayesian and a Memory-Based Approach", Proceedings of the Workshop: Machine Learning and Textual Information Access, pp.1-13,

10 ممتاز محمد علي المختار مجلة الخوارزمي الھندسیة المجلد 6 العدد 2 صفحة (2010) تصمیم مرشح Bayesian لرساي ل الدعایة یعتمد ھندسة البرامجیات ممتاز محمد علي المختار كلیة ھندسة المعلومات/ جامعة النھرین الخلاصة الانتشار االسریع و توفر السھل لخدمة البرید الاكتروني المجاني جعلا منھ وسطا مختارا لارسال برید الاعلانات الغیر مرغوبة و برید الدعایة بشكل عام. ھذه الرساي ل والمعروفة بالبرید التافھ او (spam) مشكلة متزایدة لكل من المستعملین و مزودي خدمة الانترنت.(ISP) یقدم البحث حلا لاحدى جوانب مشكلة رساي ل الدعایة البیضاء (Whitelist) القاي مة السوداء (spam) من خلال تطویر مرشح ملاي م لبرید المستفید client).( المرشح المقترح یتكون من ثلاثة اجزاء تعمل معا: القاي مة.Bayesian و مرشح (Blacklist) یسمح مرشح القاي مة البیضاء باستقبال الرساي ل البریدیة من عناوین معروفة للمستفید. بینما یمنع مرشح القاي مة السوداء استقبال الرساي ل البریدیة من عناوین عرفت بارسالھا لرساي ل الدعایة. یعتمد مرشح Bayesian في تقدیراتھ على محتوى الرساي ل ویرشح ھذه الرساي ل نسبة ال معیار (threshold) محدد سلفا. تم بناء قواعد البیانات المطلوبة بشكل جداول تخزن في خادم ال.SQL المرشح المقترح للمستفید یمكن ان یصل الى قواعد البیانات ھذه بشكل شفاف لكي یتمكن من تنفیذ الترشیح المطلوب. النظام المقترح یتعامل مع رساي ل الدعایة التي تكتب في كلتا اللغتین العربیة و الانكلیزیة و الذي یعتبر امرا ھاما للمستفیدین في منطقتنا. تم اعتماد مبادئ ھندسة البرامجیات خلال تصمیم النظام مما یجعل النظام اقل عرضة للاخطاء وادامتھ اسھل. خطوات التصمیم نفذت باستحدام نموذج Visual تم تطویر واجھة للمستفید سھلة الاستخدام للحصول على مزایا المرشح المقترح. تم استخدام بیي ة Basic 6.ASCENT وبرامجیات Waterfall لبناء النظام كما استخدم SQL Server لبناء وتنفیذ قواعد البیانات المطلوبة. تم استخدام عدد من مقاییس الاداء و استحصال النتاي ج التجریبیة مع مجموعة من البرید المجموع لتقییم الاداء للمرشح المقترح واثبات كفاي تھ. 92

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