Spam Guard : Email Spam Detection System Using Python
Yogeshwari Sham Sarsar, Dr. Girish. A. Kulkarni
SVM, E-mail, IP, TFIDF, machine learning
All E-mails have a common structure, subject of the email and the body of the email. A typical spam mail can be classified by filtering its content. The process of spam mail detection is based on the assumption that the content of the spam mail is different than the legitimate or ham mail. For example, words related to the advertisement of any product, endorsement of services, dating related content etc. The process of spam email detection can be broadly categorized into two approaches: knowledge engineering and machine learning approach. Knowledge engineering is a network-based approach in which IP address, network address along with some sets of defined rules is considered for the email classification. The approach has shown promising results but it is very time consuming. Term Frequency Inverse Document Frequency (TFIDF) based SVM system. The maintenance and task of updating rules is not convenient for all users. On the other hand, machine learning approach does not involve any set of rules and is efficient than knowledge engineering approach. The classification algorithm classifies the email based on the content and other attributes.
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Unique Paper ID: 163129

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 1065 - 1076
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