SMS Spam Detection using Machine Learning and Deep Learning Techniques

The number of people using mobile devices increasing day by day. SMS (short message service) is a text message service available in smartphones as well as basic phones. So, the traffic of SMS increased drastically. The spam messages also increased. The spammers try to send spam messages for their fi...

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Bibliographic Details
Published in2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Vol. 1; pp. 358 - 362
Main Authors Gadde, Sridevi, Lakshmanarao, A., Satyanarayana, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.03.2021
Subjects
Business
Communication systems
Computational modeling
Credit cards
Deep learning
LSTM
Machine Learning
Message service
Short Message Service
Smart phones
Spam
UCI
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Summary:The number of people using mobile devices increasing day by day. SMS (short message service) is a text message service available in smartphones as well as basic phones. So, the traffic of SMS increased drastically. The spam messages also increased. The spammers try to send spam messages for their financial or business benefits like market growth, lottery ticket information, credit card information, etc. So, spam classification has special attention. In this paper, we applied various machine learning and deep learning techniques for SMS spam detection. we used a dataset from UCI and build a spam detection model. Our experimental results have shown that our LSTM model outperforms previous models in spam detection with an accuracy of 98.5%. We used python for all implementations.
ISBN:9781665405201
1665405201
ISSN:2575-7288
DOI:10.1109/ICACCS51430.2021.9441783