This document discusses email spam detection. It begins with the objectives of classifying emails as spam or ham (legitimate emails) and providing users knowledge about fake vs real emails. It introduces spam as unsolicited commercial email involving mass mailing, explains how spammers obtain emails and the spam lifecycle. The document also outlines different types of spam filters including header, Bayesian, permission and content filters. It provides a flowchart of the email processing and preprocessing steps like removing stop words and lemmatization (grouping word forms). Finally, it discusses the scope of the project, including increased security and reduced costs.

1. EMAILSPAM
DETECTION
Submitted By
PRATISTHA SINGH

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

7. LIFECYCLE OF A SPAM

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

9. FLOWCHART OF PROCESSING

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.

12. THANK YOU