An analysis on Filter for Spam Mail

International Journal of Innovative Research in Advanced Engineering (IJIRAE)
Volume 1 Issue 1 (April 2014)
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ISSN: 2278-2311 IJIRAE | http://ijirae.com
© 2014, IJIRAE All Rights Reserved Page - 174
An analysis on Filter for Spam Mail
Anand G Sharma Prof. Vedant Rastogi
PG-Scholar/IT Associate Professor/IT
Institute of Engineering and Technology, Alwar (RAJ) Institute of Engineering and Technology, Alwar (RAJ)
Alwar, India Alwar,India
sharma.anand2008@gmail.com vedantnoki@gmail.com
Abstract— Electronic-mail is widely used most suitable method of transferring messages electronically from one
person to another, rising from and going to any part of the world. Main features of Electronic mail is its speed,
dependability, well-equipped storage options and a large number of added services make it highly well-liked
among people from all sectors of business and society. But being popular it also has negative side too. Electronics
mails are preferred media for a large number of attacks over the internet.. A number of the most popular attacks over
the internet include spams. Some methods are essentially in detection of spam related mails but they have higher false
positives. A number of filters such as Checksum-based filters, Bayesian filters, machine learning based and
memory-based filters are usually used in order to recognize spams. As spammers constantly try to find a way to
avoid existing filters, a new filters need to be developed to catch spam. This paper proposes to find an
resourceful spam mail filtering method using user profile base ontology. Ontologies permit for machine-
understandable semantics of data. It is main to interchange information with each other for more efficient spam
filtering. Thus, it is essential to build ontology and a framework for capable email filtering. Using ontology that is
particularly designed to filter spam, bunch of useless bulk email could be filtered out on the system. We propose a
user profile-based spam filter that classifies email based on the likelihood that User profile within it have been
included in spam or valid email.
Keywords— Electronic mail, spam, ontology, filter, RDF
I. INTRODUCTION
As the number of Internet user’s increases Electronic mail has been a capable and well-liked communication system.
Electronic mail is most convenient method of transmitting messages electronically from one person to other person and
going to anywhere in the world. Electronic mail connects each and every location around the world, eliminating
geographical limitations and bridging people closer. On the technical side, it includes a number of protocols such as
SMTP, TCP/IP, POP, and so on, for transferring messages from one mailbox to another mailbox. Electronic mail can
be appropriately defined as a method for interchanging digital information from one author to one or more
recipients and it has become the standard media of communication in various areas. It provides many eye-catching
features by its virtue such as simple, fast and free access, global acceptance, support for instant messaging protocols,
support for file attachments, etc. [3] Therefore, managing email became an important and growing problem for
individuals and organizations because it is prone to mistreatment. The blind posting of needless email messages is said
to be spam is an example of the misuse. Spam is normally defined as sending of unwanted bulk email - that is, email
that was not asked for by multiple recipients. A another common definition of a spam is limited to unwanted
commercial email, a definition that does not consider non-commercial solicitation such as political or religious
pitches, even if unwanted, as spam. Email was by far the most common form of spamming on the internet. According
to the data estimated by Ferris Research [1], spam accounts for 15% to 20% of email at U.S.-based corporate
organizations. Half of users are receiving 10 or more spam emails per day while some of them are receiving up to
several hundred unwanted emails. International Data Group [2] expected that global email traffic surges to 60 billion
messages daily. It involves sending identical or nearly identical unwanted messages to a large number of recipients.
Unlike valid commercial email, spam is generally sent without the explicit permission of the recipients, and
frequently contains various tricks to bypass email filters.
II. LITERATURE SURVEY
1. MEANING OF AN ONTOLOGY
Ontology is an open specification of a conceptualization. Ontologies can be taxonomic hierarchies of classes, class
definitions, but need not be limited to these forms. Also, ontologies are not limited to conservative definitions. To
specify a conceptualization one needs to state principle that constraint the possible interpretations for the defined
terms. Ontologies play a key role in capturing domain knowledge and providing a common understanding. Generally,
ontologies consist of classified class hierarchy, domain knowledge base, and relationships between classes and
instances. An ontology has different relationships depending on the schema or classification builder, and it has
different restrictions depending on the language used. Also, the domain, range, and cardinality are different based on
ontology builder. Ontologies permit for machine-understandable semantics of data, and make the effective search,
interchange, and addition of knowledge for business-to-business and business to- consumer (B2C) e-commerce. By using
semantic data, the usability of e-technology can be facilitated. There are several languages such as extensible markup

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language (XML), resource description framework (RDF), RDF schema (RDFS), DAML+OIL, and OWL. Many tools
have been developed for implementing metadata of ontologies using these languages. However, current tools have
problems with interoperation and association.
2. DEVELOPMENT OF ONTOLOGY
Ontology development tools can be applied to all phases of the ontology lifecycle together with the formation,
implementation and maintenance of ontologies. An ontology can be used to maintain various types of knowledge
management including knowledge recovery, sharing and storage. In one of the most accepted definitions, ontology is
the specification of shared knowledge. For a knowledge management system, ontology can be defined as the
categorization of knowledge. Ontologies are different from conventional keyword-based search engines in that they are
metadata, capable to afford the search engine with the functionality of semantic match. Ontologies are able to search
more efficiently than usual methods. Basically, ontology consists of hierarchical explanation of main concepts in a
domain and the descriptions of the properties of each concept. Traditionally, ontologies are built by both skilled
knowledge engineers and domain specialists who may not be well-known with computer software. Ontology creation is a
time-consuming task. Its tools require users to be trained in knowledge representation and guess logic. XML is not
suited to describe machine understandable documents and interrelationships of resources in ontology [7]. Ontology tools
have to tolerate more expressive power and scalability with a large knowledge base, matching and reasoning in querying.
Also, they need to support the use of high-level language, visualization, modularity.
3.IDENTIFYING SPAM MAIL
A algorithm was developed to decrease the feature space without sacrificing remarkable correctness, but the usefulness
was based on the excellence of the training dataset, demonstrated that the feasibility of the approach to find the best
learning algorithm and the metadata to be used, which is a very significant contribution in email categorization
using Rainbow system[8]. A graph based mining approach for email categorization structures/patterns can be extracted
from a pre-classified email folder and the same can be used effectively for categorizing incoming email messages
[7].Which are spam-specific features in their work, could improve the categorization results. A good performance
was obtained by removing the classification error by finding temporal relations in an email sequence in the form of
temporal sequence patterns and embedding the discovered information into content-based learning methods. It shows
the work on spam filtering using feature selection based on heuristics. It introduced a method to help various classifiers
to increase the mining of classified profiles. Upon receiving a document, the technique helps to create dynamic
classified profiles with respect to the document, and accordingly helps to make proper filtering and categorization
decisions.
III. PROPOSED WORK
The training datasets are the set of emails that gives us a categorization result. The test data is actually the email that will
run through our system which we test to see if classified correctly as spam or not. This will be an ongoing test
process and so, the test data is not finite because of the learning procedure, and will sometimes merge with the training
data. To do that, the training datasets should be modified as a compatible input format. To query the test email,
an ontology should be created based on the categorization result. To create ontology, an ontology language is
required. MDF will be use to create an ontology. The categorization result of MDF format will be inputted to Jena, and
inputted MDF will deploy through Jena, finally, an ontology will be created. An ontology generated in the form of
MDF data model is the base on which the incoming mail is checked for its legitimacy. Depending upon the assertions
that we can conclude from the outputs of Jena, the email can be defined as spam or valid. Customized ontology filter
would be different with each other depending on each user’s background, preference, etc. That means one email
might be spam for person A, but not for person B. The ontology evolves periodically and adaptively. The input to
the system is mainly the training datasets and then the test email. The test email is the first set of emails that the system
will classify and learn and after a certain time, the system will take a variety of emails as input to be filtered as
spam or valid. The ontology technique enables us to decide the way different headers and the data inside the email are
linked based upon the word frequencies of each words or characters in the datasets. The mapping also enables us to
obtain assertions about the legitimacy and non-legitimacy of the emails. The next part is using this ontology to
decide whether a new email is spam or valid. Queries using the obtained ontology will process again through Jena.
The output obtained after querying will be the decision that the new email is spam or valid [11].
1. ARCHITECTURE OF SPAM FILTER

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Figure 1 Schematic representation of spam filters.
Figure 1 shows our framework to filter spam. The training dataset is the set of email that gives us a categorization result.
The test data is actually the email will run through our system which we test to see if classified correctly as spam or not.
This will be an ongoing test process and so, the test data is not finite because of the learning procedure, the test data will
sometimes merge with the training data. The training dataset was used as input to categorization algorithm. To do that,
the training dataset should be modified as a compatible to query the test email in Jena, an ontology should be
created based on the categorization result. To create ontology, an ontology language was required. MDF was used to
create ontology. The categorization result in the form of MDF file format was inputted to Jena, and inputted RDF was
deployed through Jena, finally, ontology was created. Ontology generated in the form of MDF data model is the base on
which the incoming mail is checked for its legitimacy. Depending upon the assertions that we can conclude from the
outputs of Jena, the email can be defined as spam or otherwise. The email is actually the email in the format that Jena
will take in (i.e. in a CSV format) and will run through the ontology that will result in spam or not spam. The input to the
system mainly is the training dataset and then the test email. The test email is the first set of emails that the system will
classify and learn and after a certain time, the system will take a variety of emails as input to be filtered as a spam or not.
The training dataset which we used, which had categorized values for features on the basis of which the
decision tree will classify, first be given to get the same. The categorization results need to be converted to
ontology. This ontology enabled us to decide the way different headers and the data inside the email are linked based
upon the word frequencies of each words or characters in the dataset. The mapping also enabled us to obtain assertions
about the legitimacy and no legitimacy of the emails. The next part was using this ontology to decide whether a new
email is a spam or not. This required querying of the obtained ontology which was again done through Jena. The output
obtained after querying was the decision that the new email is a spam or not [15].
2 SPAM IDENTIFICATION METHODS
The several different methods to identify incoming messages as spam are, White list/Blacklist, verifying URL ,
Checking Mail header, Keyword checking.
A. WHITE LIST/BLACKLISTS
White list is a list, which contains all addresses from which we always wish to functional domains. white list
management tool that eliminates the need for administrators to manually input approved addresses on the white list and
ensures that mail from particular senders or domains are never flagged as spam. The number of records can be
configured. When an overflow occurs, obsolete records are overwritten. A blacklist works similarly to competitive
alternatives: this is a list of addresses from which we never want to receive mail [15].
B. KEYWORD CHECKING
Another method widely used in filtering spam. It works by scanning both email subject and body. Using "conditions" i.e.
combinations of keywords is a good solution to enhance filtering efficiency. We can specify combinations of words and
update the list that must appear in the spam email. All messages that include these words will be blocked [9].
C. CHECKING MAIL HEADER
This technique consists of a set of rules that, if a mail header matches, triggers the mail server to return
messages that have blank "From" field, that lists a lot of addresses in the "To" from the same source, that
have too many digits in email addresses (a fairly popular method of generating false addresses). It also enables to
return messages by matching the language code stated in the header.
D. VERIFYING URL
Verifying URL email can be classified on base of URL. Blocks all the mails from the list of addresses from which user
never want to receive mail.
IV. APPLICATION
1. Effective email client

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2. Private White list and blacklist. User can maintain their own lists of secured email senders (white list) and suspicious
email addresses, from which you are used to receive spam (blacklist). User can update list whenever want to update.
3. Certain spam solutions make the choice about spam emails not only based on the sender's mail address, they also
analyze the subject lines and the message content and remove the spam mail before entering to inbox
4. Elimination of virus from emails
5. Provide the user only required content.
V. CONCLUSION
In this paper, we present Spam or unwanted Electronic mail has become a major problem for organizations and
private users. From the study we found that, many of the filtering techniques has been proposed for sorting the
mails are based on Blacklist, White list, Bayesian filter methods and there is no method can maintain to offer an ideal
solution. Our approach is relay on applying profile base categorization techniques by means of Ontology tools. There is
lot of scale for research in classifying text messages as well as multimedia messages. A personalized ontology filter was
evolved based on specific user’s background. Hence, as expected, better sorting for spam can be achieved using a
customized ontology filter which is scalable. Text oriented email datasets are adapted, but the same idea can be
applicable to other categorization work.
REFERENCES
[1] Ferris Research. Spam Control: Problems & Opportunities, 2003.
[2] Lourdes Araujo and Juan Martinez-Romo, Web Spam Detection: New Categorization Features Based on Qualified
Link Analysis and Language Models. Proceeding of IEEE Transactions on information forensics and
security,vol.5, no. 3, September.
[3] Take, Aura Kumar & Tapas, Shashikala, (2010) “Knowledge Base Compound Approach towards Spam
Detection”, Recent Trends in Network Security and Applications, Communications in Computer and Information
Science, Vol 89, Part 2, pp 490-499, DOI=10.1007/978-3-642-14478-3_49, Published by Springer Berlin,
Heidelberg
[4] Giorgio Fumera, Ignazio Pillai, Fabio Roli, Spam Filtering Based On The Analysis Of Text Information
Embedded Into Images. Proceedind of Journal of Machine Learning Research 7 (2006) 2699-2720.
[5] Youn, S, and McLeod, D. Ontology Development Tools for Ontology-Based Knowledge
Management.Encyclopedia of E-Commerce, E-Government and Mobile Commerce, Idea Group Inc, 2006.
[6] Seongwook Youn, Dennis McLeod, Spam Email Categorization using an Adaptive. Proceedind of Journal of
Software, vol.2, no.3, September.
[7] Shankar, S., and Karypis, G. Weight adjustment schemes for a centroid based classifier. Computer
ScienceTechnical Report TR00-035, 2000.
[8] Yang, J., Chalasani, V., and Park, S. Intelligent Email Categorization Based on Textual Information and
Metadata. IEICE TRANS. INF. & SYST., VOL. E82, NO.1 JANUARY 1999.
[9] Rathore, Shashikant, Jassi, Palvi & Agarwal, Basant, (2011) “A New Probability based Analysis for Recognition of
Unwanted Electronic mails”, International Journal of Computer Applications (IJCA),Vol. 28, Issue 4,
Published by Foundation of Computer Science, New York, USA.
[10] Luo, Yan, (2010) “Workload characterization of Spam Electronic mail Filtering Systems”, International Journal
of Network Security & Its Applications (IJNSA), Vol. 2, No.1.
[11] Yang, J., Chalasani, V., and Park, S. Intelligent Email Categorization Based on Textual Information and
Metadata. IEICE TRANS. INF. & SYST., VOL. E82, NO.1 JANUARY 1999.
[12] Sakkis, G., Androutsopoulos, I., & Paliouras, G., (2003) “A Memory based Approach to Antispam Filtering”,
Information Retrieval, Vol 6, pp 49-73.
[13] Saraubon, Kobkiat & Limthanmaphon, Benchaphon, (2009) “Fast Effective Botnet Spam Detection”, Fourth
International Conference on Computer Sciences and Convergence Information Technologies (ICCIT), pp 1066-
1070.
[14]Pundt, H., and Bishr, Y. Domain ontologies for data sharing: An example from environmental monitoring using field
GIS. Computer & Geosciences, 28, 98-102, 1999.
[15] Jindal, Nitin & Liu, Bing, (2007) “Analyzing and Detecting Review Spam”, Seventh IEEEInternational
Conference on Data Mining (ICDM), Published by IEEE.

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