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EMAILSPAM
DETECTION
Submitted By
PRATISTHA SINGH
CONTENT
OBJECTIVE
INTRODUCTION
SPAM AS A PROBLEM
OBTAINING EMAIL THROUGH
VARIOUS METHODS
LIFECYCLE OF SPAM
TYPES OF SPAM FILTERS
FLOWCART OF PROCESSING
DOCUMENT PREPROCESSING
SCOPE OF THE PROJECT
OBJECTIVE
To give knowledge to the user
about fake emails and relevant
emails.
To classify the mail as spam or
ham.
INTRODUCTION
What is SPAM?
 Spam also called as Unsolicited
Commercial Email(UCE)
 Involves sending message by email
to numerous recipients at the same
time (Mass Emailing)
 Grew exponentially since 1990
 80% of all spam is sent by less than
200 spammers.
Purpose of SPAM
 Advertisement
 Pyramid Schemes(Multi
Level Marketing)
 Giveaways
 Chain Letters
 Political Email
 Stock Market Advice
SPAM AS A PROBLEM
 Consumes
computing
resources and
time.
 Reduces the
effectiveness of
legitimate
advertising
 Cost Shifting
 Fraud
 Identity Theft
 Consumer
Perception
 Global
Implications
OBTAINING EMAIL THROUGH VARIOUS
METHODS
Purchasing/ Trading
lists with other
spammers
Bots
Directory harvest
attack
Free Product or
Services requiring valid
email address
News
bulletins/Forums
LIFECYCLE OF A SPAM
TYPES OF SPAM FILTERS
Header Filters
 Look at email
headers to judge if
forged or not.
 Contain more
information in
addition to recipient,
sender and subject
fields.
Bayesian Filters
Statistical email
filtering
Uses Naïve Bayes
Classifier
Permission Filters
Based on challenge/
Response system
Content Filters
Scan the text content
of emails
Use fuzzy logics
Language Filters
Filters based on email
body language
Can be used to filter out
spam written in foreign
languages
FLOWCHART OF PROCESSING
DOCUMENT PREPROCESSING
Removal of Stop Words
Sometimes, the
extremely common word
which would appear to be of
very little value in helping
select documents
matching user need are
excluded from the
vocabulary entirely.
Lemmatization
Lemmatization linguistics, is the
process of grouping together
the different inflected forms of
a word so they can be analyzed
as a single item.
Tokenization
Tokenization is the process of
breaking a stream of text up
into words, phrases, symbols,
or other meaningful elements
called tokens.
SCOPE OF THE PROJECT
 It provides sensitivity to the client and adapts well to the future spam techniques.
 It considers a complete message instead of single words with respect to its organization.
 It increases Security and Control.
 It reduces IT Administration Costs.
 It also reduces Network Resource Costs.
THANK YOU

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Email spam detection

  • 2. CONTENT OBJECTIVE INTRODUCTION SPAM AS A PROBLEM OBTAINING EMAIL THROUGH VARIOUS METHODS LIFECYCLE OF SPAM TYPES OF SPAM FILTERS FLOWCART OF PROCESSING DOCUMENT PREPROCESSING SCOPE OF THE PROJECT
  • 3. OBJECTIVE To give knowledge to the user about fake emails and relevant emails. To classify the mail as spam or ham.
  • 4. INTRODUCTION What is SPAM?  Spam also called as Unsolicited Commercial Email(UCE)  Involves sending message by email to numerous recipients at the same time (Mass Emailing)  Grew exponentially since 1990  80% of all spam is sent by less than 200 spammers. Purpose of SPAM  Advertisement  Pyramid Schemes(Multi Level Marketing)  Giveaways  Chain Letters  Political Email  Stock Market Advice
  • 5. SPAM AS A PROBLEM  Consumes computing resources and time.  Reduces the effectiveness of legitimate advertising  Cost Shifting  Fraud  Identity Theft  Consumer Perception  Global Implications
  • 6. OBTAINING EMAIL THROUGH VARIOUS METHODS Purchasing/ Trading lists with other spammers Bots Directory harvest attack Free Product or Services requiring valid email address News bulletins/Forums
  • 8. TYPES OF SPAM FILTERS Header Filters  Look at email headers to judge if forged or not.  Contain more information in addition to recipient, sender and subject fields. Bayesian Filters Statistical email filtering Uses Naïve Bayes Classifier Permission Filters Based on challenge/ Response system Content Filters Scan the text content of emails Use fuzzy logics Language Filters Filters based on email body language Can be used to filter out spam written in foreign languages
  • 10. DOCUMENT PREPROCESSING Removal of Stop Words Sometimes, the extremely common word which would appear to be of very little value in helping select documents matching user need are excluded from the vocabulary entirely. Lemmatization Lemmatization linguistics, is the process of grouping together the different inflected forms of a word so they can be analyzed as a single item. Tokenization Tokenization is the process of breaking a stream of text up into words, phrases, symbols, or other meaningful elements called tokens.
  • 11. SCOPE OF THE PROJECT  It provides sensitivity to the client and adapts well to the future spam techniques.  It considers a complete message instead of single words with respect to its organization.  It increases Security and Control.  It reduces IT Administration Costs.  It also reduces Network Resource Costs.