eprintid: 18690
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/86/90
datestamp: 2024-06-04 14:11:03
lastmod: 2024-06-04 14:11:03
status_changed: 2024-06-04 14:03:52
type: article
metadata_visibility: show
creators_name: Akande, O.N.
creators_name: Gbenle, O.
creators_name: Abikoye, O.C.
creators_name: Jimoh, R.G.
creators_name: Akande, H.B.
creators_name: Balogun, A.O.
creators_name: Fatokun, A.
title: SMSPROTECT: An automatic smishing detection mobile application
ispublished: pub
note: cited By 6
abstract: Short Messaging Service (SMS) has grown to become the most widely used feature in mobile devices. The technological advancements that birthed other alternative messaging applications have not been able to phase out the use of the SMS. However, hackers have been exploiting this SMS feature to perpetrate smishing acts. Existing research has focused on how spam SMS could be detected and separated from ham messages but have not really done much at preventing the act of smishing. Therefore, this research presents a mobile application that used a rule-based SMS service to detect and prevent smishing attacks. Specifically, the developed SMS service allows the developed SMS mobile application to intercept incoming SMS to a smartphone. The intercepted messages were then forwarded through an Application Programming Interface (API) to the rule-based machine learning model. The model uses the carefully selected rules to analyze the retrieved message and asserts if it is a spam or ham. The result of the analysis is then forwarded to the mobile application through the API. However, the final decision to retain or discard the spam or ham depends on the user after receiving notification from the user. © 2022 The Author(s)
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131818173&doi=10.1016%2fj.icte.2022.05.009&partnerID=40&md5=8075749a3dc0b872735b160c5f628aa7
id_number: 10.1016/j.icte.2022.05.009
full_text_status: none
publication: ICT Express
volume: 9
number: 2
pagerange: 168-176
refereed: TRUE
citation:   Akande, O.N. and Gbenle, O. and Abikoye, O.C. and Jimoh, R.G. and Akande, H.B. and Balogun, A.O. and Fatokun, A.  (2023) SMSPROTECT: An automatic smishing detection mobile application.  ICT Express, 9 (2).  pp. 168-176.