Multi-lingual Spam SMS detection using a hybrid deep learning technique

Title

Multi-lingual Spam SMS detection using a hybrid deep learning technique

Creator

Ramanujam E.; Shankar K.; Sharma A.

Description

Nowadays, the incremental usage of mobile phones has made spam SMS messages a big concern. Sending malicious links through spam messages harms our mobile devices physically, and the attacker might have a chance to steal sensitive information from our devices. Various state-of-the-art research works have been proposed for SMS spam detection using feature-based, machine, and deep learning techniques. These approaches have specific limitations, such as extracting and selecting signifi-cant and quality features for efficient classification. Very few deep learning techniques are only used for classifying spam detection. Moreover, the benchmark spam datasets written in English are mostly used for evaluation. Very few papers have detected spam messages for other languages. Hence, this paper creates a multilingual SMS spam dataset and proposes a hybrid deep learning technique that combines the Convolutional Neural Network and Long Short-Term Memory (LSTM) model to classify the message dataset. The performance of this proposed hybrid model has been compared with the baseline deep learning models using accuracy, precision, recall, and F1-score metrics. 2022 IEEE.

Source

Proceedings - 2022 IEEE Silchar Subsection Conference, SILCON 2022

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

deep learning; ham; machine learning; smishing; spam; Spam detection

Coverage

Ramanujam E., National Institute of Technology Silchar, Computer Science and Engineering, Assam, India; Shankar K., National Institute of Technology Silchar, Electronics and Instrumentation Engineering, Assam, India; Sharma A., Computer Science and Engineering, Christ (Deemed to Be) University, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 978-166547100-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/SILCON55242.2022.10028936

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147854946&doi=10.1109%2fSILCON55242.2022.10028936&partnerID=40&md5=ec29cdc2d6e81114e170bd9792238392