Authors :
Lokam. Devi Naga Srinu; Meesala. Dhanush Kumar; Mulaparthi. Mani Gopal
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/y9nttx6p
Scribd :
https://tinyurl.com/4htutmpe
DOI :
https://doi.org/10.5281/zenodo.10795930
Abstract :
With the spread of modern life, messaging has
become one of the most important forms of
communication. SMS (Short Message Service) is a text
messaging service available on all smart phones and
mobiles. Facebook, WhatsApp etc. Unlike other chat-
based communication applications, SMS does not require
any internet connection. SMS traffic has increased
significantly and spam has also been increased rapidly.
Hackers and spammers are trying to scam over devices
through SMSs. As a result, SMS support for mobile
devices becomes difficult. Spammers may ask for business
expansion, lottery information, credit card information,
etc. They also try to send spam emails to obtain financial
or commercial benefits such as: attackers attempt to
disrupt the system by sending spam links that, when
clicked, allow them to control mobile devices. To analyze
this communication, the authors developed a system that
can analyze malicious messages and determine whether
they are RAW or SPAM. Here, we use text classification
methods such as Naive Bayes classifier algorithm to
classify the texts and determine the message whether it is
spam or not.
Keywords :
Machine Learning, Language Processing, Spam, Ham, SMS, Naive Bayes, Logistic Regression.
With the spread of modern life, messaging has
become one of the most important forms of
communication. SMS (Short Message Service) is a text
messaging service available on all smart phones and
mobiles. Facebook, WhatsApp etc. Unlike other chat-
based communication applications, SMS does not require
any internet connection. SMS traffic has increased
significantly and spam has also been increased rapidly.
Hackers and spammers are trying to scam over devices
through SMSs. As a result, SMS support for mobile
devices becomes difficult. Spammers may ask for business
expansion, lottery information, credit card information,
etc. They also try to send spam emails to obtain financial
or commercial benefits such as: attackers attempt to
disrupt the system by sending spam links that, when
clicked, allow them to control mobile devices. To analyze
this communication, the authors developed a system that
can analyze malicious messages and determine whether
they are RAW or SPAM. Here, we use text classification
methods such as Naive Bayes classifier algorithm to
classify the texts and determine the message whether it is
spam or not.
Keywords :
Machine Learning, Language Processing, Spam, Ham, SMS, Naive Bayes, Logistic Regression.
