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Social Network Analysis and Mining
Published in:

01-12-2023 | Original Article

An investigation in detection and mitigation of smishing using machine learning techniques

Authors: Mohd Shoaib, Mohammad Sarosh Umar

Published in: Social Network Analysis and Mining | Issue 1/2023

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Abstract

The article delves into the growing threat of smishing, which involves malicious text messages targeting mobile users. It discusses the prevalence of smishing due to the widespread use of mobile phones and the vulnerabilities they present. The study explores various detection methods, including machine learning techniques like neural networks and signature matching. It also highlights the limitations of current approaches and the need for more advanced algorithms. The research analyzes the performance metrics and datasets used in different studies, providing a comparative overview of the field. Additionally, it discusses the challenges faced in smishing detection, such as the difficulty in distinguishing between spam and smishing messages, and proposes future directions for research, including the development of more effective classifiers and hybrid algorithms.
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Metadata
Title
An investigation in detection and mitigation of smishing using machine learning techniques
Authors
Mohd Shoaib
Mohammad Sarosh Umar
Publication date
01-12-2023
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2023
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-023-01142-4

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