Enhancing SMS Spam Detection Using Deep Learning Models: A Comparative Study

Singh Sikarwar, Shailendra and Arivukkodi, R. and Krishnane, Dhanya and Sharma, Himanshu and Namdeo, Avinash and Jadon, Kaushiki (2024) Enhancing SMS Spam Detection Using Deep Learning Models: A Comparative Study. In: 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), Greater Noida, India.

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Abstract

Abstract—The rise in Machine Learning algorithms for iden-
tifying SMS spam has become popular due to a significant surge in unwanted text messages. Detecting SMS spam holds crucial importance for several reasons. Firstly, it can inundate mobile phone users’ message inboxes with irrelevant and unwanted messages, causing frustration and irritation. Secondly, SMS spam serves as a conduit for phishing scams, where scammers exploit fraudulent SMS messages to deceive users into divulging personal information or downloading malicious software. These scams can result in financial losses, identity theft, or other forms of fraud.
Lastly, SMS spam can propagate malware or viruses, potentially compromising the security or functionality of the user’s device.

Item Type: Conference or Workshop Item (Paper)
Subjects: AI AND DS > Deep Learning
Divisions: Engineering > Computer Science and Engineering
Depositing User: Unnamed user with email techsupport@mosys.org
Date Deposited: 01 Aug 2026 12:54
Last Modified: 01 Aug 2026 12:54
URI: https://ir.dsce.ac.in/id/eprint/175

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