A Review on Artificial Intelligence Techniques for Multilingual SMS Spam Detection

Title

A Review on Artificial Intelligence Techniques for Multilingual SMS Spam Detection

Creator

Ramanujam E.; Shankar K.; Sharma A.

Description

With social networks increased popularity and smartphone technology advancements, Facebook, Twitter, and short text messaging services (SMS) have gained popularity. The availability of these low cost text-based communication services has implicitly increased the intrusion of spam messages. These spam messages have started emerging as an important issue, especially to short-duration mobile users such as aged persons, children, and other less skilled users of mobile phones. Unknowingly or mistakenly clicking the hyperlinks in spam messages or subscribing to advertisements puts them under threat of debiting their money from either the bank account or the balance of the network subscriber. Different approaches have been attempted to detect spam messages in the last decade. Many mobile applications have also evolved for spam detection in English, but still, there is a lack of performance. As English has been completely covered under natural language processing, other regional languages, such as Urdu and Hindi variants, have specific issues detecting spam messages. Mobile users suffer greatly from these issues, especially in multilingual countries like India. Thus, this paper critically reviews the artificial intelligence-based spam detection system. The review lists out the existing systems that use machine and deep learning techniques with their limitations, merits, and demerits. In addition, this paper covers the scope for future enhancements in natural language processing to efficiently prevent spam messages rather than detect spam messages. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Electrical Engineering, Vol-1087, pp. 525-536.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Deep learning; Ham; Machine learning; NLP; Phishing; Smishing; SMS; Spam; Spam detection

Coverage

Ramanujam E., Computer Science and Engineering, National Institute of Technology Silchar, Assam, Silchar, 788010, India; Shankar K., Electronics and Instrumentation Engineering, National Institute of Technology Silchar, Assam, Silchar, 788010, India; Sharma A., Department of Computer Science and Engineering, Christ (Deemed to be) University, Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 18761100; ISBN: 978-981996689-9

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-99-6690-5_40

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184126718&doi=10.1007%2f978-981-99-6690-5_40&partnerID=40&md5=9198e0130c57f870011c868962fd9f9e