Enhanced Spam Detection in Short Message Service using Hybrid Techniques

Title

Enhanced Spam Detection in Short Message Service using Hybrid Techniques

Creator

Raj, E. Babu; Barona, R.; Mary, N. Ansgar; George, Shiju

Description

Receiving unwanted text messages, or SMS spam, costs consumers time and money and poses a security concern. To address this issue, we can deploy a system that recognizes and automatically filters out undesirable messages. This method, a testament to the advancement in technology, employs machine learning algorithms that gain knowledge from a pool of communications classified as spam or not. Managing various message contents and languages is one of the system's unique challenges. Notwithstanding these challenges, the approach may be effective in reducing unsolicited communications, improving the security of people's mobile devices and saving them time and money. To address this issue, a variety of machine learning approaches have been employed, ranging from more modern deep learning methods like Convolutional Neural Networks (CNNs) to more traditional ones like Naive Bayes. It is common practice to assess the effectiveness of SMS spam classifiers using measures like as F1-score, precision, and recall. All things considered, SMS spam classification is crucial for protecting the security and privacy of mobile phone users and has useful applications in everyday situations. Grenze Scientific Society, 2025.

Source

16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;1;pp.4203-4209

Date

01-01-2025

Publisher

Grenze Scientific Society

Subject

Convolutional Neural Network (CNN); Machine Learning; Nae Bayes; SMS Spam

Coverage

Raj E.B., Christ University, Bengaluru, Bengaluru, India; Barona R., St. Xavier's Catholic College of Engineering, IT, Chunkankadai, India; Mary N.A., St. Xavier's Catholic College of Engineering, IT, Chunkankadai, India; George S., Christ University, Bengaluru, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Format

online

Language

English

Type

Conference paper

Identifier

https://www.scopus.com/pages/publications/105034072843?origin=resultslist