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ISSN: 2229-6956(ONLINE) ICTACT JOURNAL ON SOFT COMPUTING, APRIL 2012, VOLUME: 02, ISSUE: 03 AN EFFECTIVE SPAM FILTERING FOR DYNAMIC MAIL MANAGEMENT SYSTEM
S. Arun Mozhi Selvi1 and R.S. Rajesh2
1Department of Information Technology, Dr. Sivanthi Aditanar College of Engineering, India 2Department of Computer Science and Engineering, Manonmaniam Sundaranar University, India Abstract
for major retail outlets and small artisans and traders. Business- Spam is commonly defined as unsolicited email messages and the goal
to-business and financial services on the Internet affect supply of spam categorization is to distinguish between spam and legitimate
email messages. The economics of spam details that the spammer has
to target several recipients with identical and similar email messages.

1.2 EMAIL
As a result a dynamic knowledge sharing effective defense against a
substantial fraction of spam has to be designed which can alternate

Electronic mail, commonly called email or e-mail, is a the burdens of frequent training stand alone spam filter. A weighted
method of exchanging digital messages across the Internet or email attribute based classification is proposed to mainly focus to
other computer networks. Originally, email was transmitted encounter the issues in normal email system. These type of
directly from one user to another computer. This required both classification helps to formulate an effective utilization of our email
computers to be online at the same time, a la instant messaging. system by combining the concepts of Bayesian Spam Filtering
Algorithm, Iterative Dichotmiser 3(ID3) Algorithm and Bloom Filter.

Today's email systems are based on a store-and-forward model. The details captured by the system are processed to track the original
Email servers accept, forward, deliver and store messages. Users sender causing disturbances and prefer them to block further mails
no longer need be online simultaneously and need only connect from them. We have tested the effectiveness of our scheme by
briefly, typically to an email server, for as long as it takes to collecting offline data from Yahoo mail & Gmail dumps. This
send or receive messages. An email message consists of two proposal is implemented using .net and sample user-Id for knowledge
components, the message header, and the message body, which base.
is the email's content. The message header contains control information, including, minimally, an originator's email address Keywords:
and one or more recipient addresses and the body contains the Spam, Bayesian, IMAP, ID3
message itself as unstructured text; sometimes containing a signature block at the end. This is exactly the same as the body 1. INTRODUCTION
of a regular letter. The header is separated from the body by a blank line. In this modern society all are spending their most of the time with internet, the reason behind this is it provides a easy way of 1.3 HOW SPAM FILTERING SYSTEM WORKS
communication with the people where ever they are and also people find a way for buying and selling their product through There is no one specific algorithm for statistically internet to make money without wasting their time as much as. determining whether or not a given e-mail message is in fact a The main criterion for this is Providing Security. Especially the spam message. As discussed earlier, the most prominent email system is suffered with degraded quality of service due to approach to spam classification involves the implementation of rampant spam and fraudulent emails. Thus in order to avoid
the Bayesian chain rule, also known as Bayesian filtering. these types of problem a system is needed to extract only the needful information for the user as per his/her requirement and 1.4 MOTIVATION
preferences. By doing this most of the unwanted mails from the The Existing system still confuses us in working with our mail user agent can be filtered to our notice which will be a great mailbox. The major part of the page holds the unwanted use for the user while viewing their regular mails. newsletters and advertisement Though there are certain packages 1.1 INTERNET
helpful to extract the needful information they are not up to the users full satisfaction and also act as a spyware which totally The Internet is a global system of interconnected computer upset’s the user. There exists a strong call to design high- networks that use the standard Internet Protocol Suite (TCP/IP) to performance email filtering systems. A careful analysis of spam serve billions of users worldwide. It is a network of networks that shows that the requirements of an efficient filtering system consists of millions of private, public, academic, business, and include: (1) accuracy (2) self-evolving capability (3) high- government networks, of local to global scope, that are linked by performance which needs to be completed quickly especially in a broad array of electronic and optical networking technologies. large email or messaging systems. We are motivated by the The Internet carries a vast range of information resources and inadequate classification speed of current anti-spam systems. services, such as the inter-linked hypertext documents of the Data have shown that the classification speeds of current spam World Wide Web (WWW) and the infrastructure to support filters fall far behind the growth of messages handled by servers. electronic mail. The Internet has enabled or accelerated new Based on this a system has to be proposed for an efficient spam forms of human interactions through instant messaging, Internet forums, and social networking. Online shopping has boomed both S ARUN MOZHI SELVI AND R S RAJESH: AN EFFECTIVE SPAM FILTERING FOR DYNAMIC MAIL MANAGEMENT SYSTEM 2. BACKGROUND AND RELATED WORKS
By the inadequate classification speed of current anti-spam SMTP Protocol
systems data have shown that the classification speeds of current Processing
spam filters fall far behind the growth of messages handled by servers. Based on this a system has to be proposed for an efficient spam filtering. From [1] the Decision tree data mining technique is chosen to classify the mails based on the any score or weight. From [2] Hash based lookup for the token in the scan Tokenisation
Database
list is chosen to improve the speed and efficiency. From [3] the basic spam filtering process for parsing the tokens of each mail in an effective manner. From [4] learnt to adapt the system under White list, Black
partial online supervision so that the efficiency may be improved on usage. From [5] a new concept of categorizing the mail into an unclassified category which is neither SPAM nor HAM. Occurrence
Thus based on the survey made a system should act as an Statistics
interface to the mail server and classifies mails as per the user’s requirements. Mails, the user always want to read are placed Statistical
under regular and those mails the user never wants to read are Algorithm
placed under spam. The unexpected mails that the user wants to get but which are not much important can be placed under suspected mails. Thus based on this classification can be done by an effective filtering mechanism by combining the concepts of Classification
Bayesian Spam Filtering Algorithm, Iterative Dichotmiser 3 Algorithm and Bloom Filter. Owing to this a system is created as a knowledge base for spam tokens which repeatedly occur in the spam mails. The probability of occurrence of such tokens are Fig.1. Work flow of the Spam Filtering System calculated using Bayesian algorithm and the output of it will be the input to the bloom filter which assigns weight for those 1.5 PROBLEM STATEMENT
tokens for the easy lookup in the knowledge base. Based on the above information and several attributes like From_Id, Subject, Most of the existing research focuses on the design of Body, To_Id, Sender’s IP Address the mails are further protocols, authentication methods; neural network based self- classified into three categories as White_List (HAM), learning and statistical filtering. In contrast, we address the spam Gray_List(SUSPECTED) , Black_List (SPAM) with a help of filtering issues from another perspective – improving the id3 algorithm. These type of classification helps to formulate an effectiveness by an efficient algorithm. They focus only towards effective utilization of our email system. This proposal is the better improvement of acquiring the mail box information implemented using .net and sample user-Id for knowledge base. from spam mails. This system is mainly to overcome the difficulties faced by the current mail server agents. The system 3. PROPOSED MECHANISM
acts as an interface to the mail server and captures the mail information as per the user’s requirement which in turn avoids Based on the survey related to classification an effective advertisement, unwanted mails from reading and wasting the spam filtering mechanism is proposed by combining the time working with large stuff of information dumped in mailbox. concepts of Bayesian Algorithm, Iterative Dichotmiser 3 A weighted email attribute based classification is proposed to Algorithm, weighted attribute algorithm and Bloom Filter. mainly focus to encounter the issues in normal email system. It Owing to this a system is created as a knowledge base for spam makes the user to feel more securable by means of detecting and tokens which repeatedly occur in the spam mails. The classifying such malicious mails when the user checks the inbox probability of occurrence of such tokens are calculated using by notifying with different colors for spam (red), suspected Bayesian algorithm and the output of it will be the input to the (blue) and ham (green) mails. These type of classification helps
bloom filter which assigns weight for those tokens for the easy to formulate an effective utilization of our email system. lookup in the knowledge base. Based on the above information 1.6 OVERVIEW OF THE PAPER
and several attributes like From_Id, Subject, Body, To_Id, Sender’s IP Address the mails are further classified into three The thesis is organized as follows section 2 the background categories as White_List (HAM), Gray_List(SUSPECTED), and motivation of this research with the help of reference paper Black_List (SPAM) with a help of ID3 algorithm. These type of and internet. Section 3 introduces the proposed mechanism classification helps to formulate an effective utilization of our which describes the major work. Section 4 describes the email system. This proposal is implemented using .net and The mechanism flows through the following stages, ISSN: 2229-6956(ONLINE) ICTACT JOURNAL ON SOFT COMPUTING, APRIL 2012, VOLUME: 02, ISSUE: 03 tokens with spam and non-spam e-mails and then using Bayesian Learning
Training
Filtering
Acquisition
statistics to calculate the probability that an e-mail is spam or not. Rather than calculating the probability for all the tokens in the message. The list of spamminess tokens are identified by different users and evaluated for the scan list both for the subject and body of the message. 3.1 DATA ACQUISITION PHASE
In this phase the no of mails of 4 different users are studied 3.3 TRAINING PHASE
and the way they are categorized is captured. This Information is Calculate the prob
acquired from the Google and Yahoo dumps as they suffer a lot Weight calculation
for spammiminess
from the different types of spam. About 200 mails are analyzed Database
of tokens #
and the mail Information retrieved from the current mail servers (probability value is
are extracted to and given to the next Learning Phase. Based on calculated based on
the Acquired data on different e-mail accounts, the following Bayesian algorithm)
sample is shown in Table.1. From the subjects, it can be noted that some of the unwanted mails are under Ham mails (i.e. inbox). The analysis shows that 50% of the mails come under ham and the remaining 50% comes under spam. For example, 3.3.1 Probability Calculation for Spamminess Tokens
“New SBI security update”, “ICICI bank home loan” even Bayesian Theorem:
though these mails are not much important they are under To calculate the probability using Bayesian Theorem, first it regular mail. Hence in order to reduce the amount of unwanted needs to calculate the probability for individual words which is mails in inbox, an idea to classify the mails into a new category likely to be spam. This is calculated by using the following called suspected was decided. This category holds the mails that are not much important and they can be viewed separately at the user’s convenience.  Pr(S/W) probability that a message is spam knowing  Pr(S) overall probability that any given message is Pr(W/S) probability that the word “x” appears in spam  Pr(H) overall probability that any given message is not Pr(W/H)  probability that the word “x” appears in ham 3.3.2 Weight Calculation Based on Bloom Filter:
In order to find the spamminess of the mail, a Bloom filter concept called weight methodology is introduced. The weight is calculated on the basis of the probability values calculated and the severity of the tokens that were analyzed during the learning phase. The weight methodology was obtained from the concept of bloom filter. In the Bloom filter, each tokens probability is considered to be associated with value ‘w’ for storing and retrieving, when used at the end to calculate a message’s spamminess, a token’s probability value ‘w’ is approximately 3.2 LEARNING PHASE
mapped back to p. The value “w” represents the weight here. The weight is calculated with a simple equation: Identifying the
Analysis
Extraction of
tokens from
header fields
studied mails
 P = Probability of the token to be spam  W = Weight assigned for the easy lookup Bayesian Spam filtering is a statistical technique of e-mail The following table shows the sample individual tokens of filtering. It makes use of naive base classifiers to identify spam both subject fields, body their probability and weight for e-mails. Bayesian classifiers work by correlating the use of S ARUN MOZHI SELVI AND R S RAJESH: AN EFFECTIVE SPAM FILTERING FOR DYNAMIC MAIL MANAGEMENT SYSTEM Table.2 Acquired information Sample P and W value for the CRITICAL ATTRIBUTES:
tokens found in both subject and body field Attribute 1  Spam List
1: Spam id and Subject
Subject Probability Weight
Probability Weight
0: Opposite situation
Attribute 2  "To id"
1: Not my id mark
0: Opposite situation
Attribute 3  Contact List
The calculated weight is rated from 1 to 10 and the 1: From id not in Contact List
Threshold value is 5. The weight for each token is calculated in 0: Opposite situation
the Learning phase as per the severity of the token made in the Attribute 4  Subject contains Abnormal Keywords
analysis. The above values (token, probability, weight) both for 1: Presence of Abnormal Keywords
subject and body are stored into database for further filtering. 0: Opposite situation
Attribute 5  Size of the Mail
3.4 FILTERING PHASE
1: No more than 6kB
0: Opposite situation
The details learnt and calculated in the previous phase are Attribute 6  Body checking
given as the input to this filtering phase. CLASSIFICATION
3.4.1 Filtering Algorithm (A Weighted Attribute Algorithm
TARGET ATTRIBUTES:
and ID3):
The various header fields (critical attributes) and the message (usually body) are given as an input to the filtering algorithm – SUSPECTED
the algorithm used here to filter and classify the mails is Iterative Dichotmister3 (ID3). It is mathematical algorithm for building Fig.6. List of Attributes for ID3 Algorithm the decision tree. The tree should be built from the top to down, with no backtracking. Step 1: Checks the List of Spam id and Subject if 1 classifies as
3.4.1.1 A Weighted Attribute Algorithm (WAA):
Step 2: Checks the TO id with user id if 1 step3 0 classifies as A
Step 3: Check with contact List if 1 classifies as A 0 step4
 If the message has the weight (w = 0), then it means Ham Step 4: Check with subject scan list if 1goto WAA 0 step5
Step 5: Check size <6kB 1classifies as C 0 step6
If the message has the weight (w = 1 to 5), then it means Step 6: Check with body scan list if 1 goto WAA 0 classifies as A
 If the message has the weight (w > 5), then it means The cumulative weight is calculated in the algorithm when it reaches the Step 4 and Step 5 so that when the weight reaches the threshold the algorithm directly classifies rather than If any one condition is satisfied it exits the main checking all the tokens, thus improves the efficiency. 3.4.1.2 ID3 Algorithm:
3.5 LOGGING PHASE
Database
Filter for future
(suspected subjects & id)
This is the phase where all the details are logged in a file for 3.5.1 Monitoring Database:
The database table contains the suspected id and subject,
which is stored from the mail that has already came to the inbox which is filtered out and then classified that it is spam. So, in future when the mails are coming from the same id are with the same subject is automatically redirected to the spam folder instead of checking the mails with all critical attributes and then finally redirects to the spam folder. This makes the filtering process much more efficient to the mail server. ISSN: 2229-6956(ONLINE) ICTACT JOURNAL ON SOFT COMPUTING, APRIL 2012, VOLUME: 02, ISSUE: 03 4. IMPLEMENTATION
The proposed mechanism was implemented in .net platform and SQL server with the help of the 4 sample user’s and their id. Based on the feedback of those sample users’s the analysis is  Current mail classification based on the no of mails Proposed Mail Classification based on the no of mails False positive Analysis for current mail server versus  False Negative Analysis for current mail server versus  Accuracy Analysis for current mail server versus 5. RESULT ANALYSIS
Sample id's
Fig.9. Proposed Mail Classification based on the no of Mails  Id1 = heyaruna@gmail.com,  Id2 = zainabasiya@yahoo.co.uk,  Id4 = karthiga24@yahoo.in,  Id5 = muthulakshmiit27@gmail.com Analysis are made by the User’s feedback for each mail id Sample Id's
Fig.10. False positive Analysis for current mail server vs. Sample id's
Fig.8. Current mail classification based on the no of mail Sample Id's
Fig.11. False Negative Analysis for current mail server vs. S ARUN MOZHI SELVI AND R S RAJESH: AN EFFECTIVE SPAM FILTERING FOR DYNAMIC MAIL MANAGEMENT SYSTEM False Positive (FP) – Classifying or identifying a ham mail as Based on the implementation results the false positives and false negatives can be reduced gradually with the help of the logging False Negative (FN) – Classifying or identifying a spam mail as phase in acquiring the original sender details. Based on this the Accuracy is also improved for large dataset. 8. FUTURE ENHANCEMENT
This proposal can be enhanced with more no of samples with more efficient Data Mining Technique. The implementation can be worked out in the mails servers for testing the effectiveness of REFERENCES
[1] Jhy-Jian Sheu “An Efficient Two-Phase Spam Filtering Method Based on E-mail Categorization”, International Journal of Network Security, Vol. 9, No. 1, pp.34-43, 2009. [2] Zhenyu Zhong and Kang Li “Speed Up Statistical Spam Filter by Approximation”, IEEE Transactions on Computers, Vol. 60, No. 1, pp. 120 – 134, 2011. Sample Id's
[3] Yan Luo, “Workload Characterization of Spam Email Filtering System”, International Journal of Network Fig.12. Accuracy Analysis for current mail server vs. proposed Security and its Application, Vol. 2, No. 1, pp. 22 – 41, 6. DISCUSSION
Androutsopoulos “Adaptive Spam Filtering Using Only Naïve Bayes Text Classifiers”, Spam Filtering Challenge Thus from the above results it can be inferred that the % of Competition, Fifth Conference on Email and Anti-Spam, false positives and false negatives in the current mail servers can be reduced. The Accuracy is also improved for large dataset. [5] Brian whitworth and Tong Liu, “Channel E-mail: A Thus it can be concluded that the major part of the inbox in Sociotechnical Response to Spam”, IEEE Computer current mail server is with spam messages which are Society, Vol. 42, No. 7, pp. 63-71, 2009. considerably avoided in the proposed Algorithm. [6] Naresh Kumar Nagwani and Ashok Bhansali “An object oriented Email clustering model using weighted similarities 7. CONCLUSION
between email attributes”, International Journal of research and reviews in Computer Science, Vol. 1, No. 2, This proposal is mainly to focus to encounter the issues in normal email system. These types of classification help to formulate an effective utilization of our current email system.

Source: http://ictactjournals.in/paper/IJSC_Vol2_Iss3_3_Paper_325_330.pdf

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