SMSPROTECT: An automatic smishing detection mobile application

Akande, Oluwatobi Noah; Oluwadara, Gbenle; Abikoye, Oluwakemi Christiana; Jimoh, Rasheed Gbenga; Akande, Hakeem Babalola; Balogun, Abdullateef Oluwagbemiga; Anuoluwapo, Fatokun

doi:10.1016/j.icte.2022.05.009

Cited by 17 publications

(4 citation statements)

References 10 publications

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“…The application, developed in a popular programming language and supported by an API, was designed to intercept and evaluate SMS, providing alerts to users. The results showed high precision in spam identification, demonstrating the feasibility and effectiveness of this solution for preventing smishing on mobile devices [1].. In a study conducted in China, researchers investigated vulnerabilities in biometric authentication systems on smartphones, focusing on an innovative method to compromise fingerprint authentication.…”

Section: Of 31mentioning

confidence: 94%

“…While studies on cybercrime exist in other countries, there is a lack of research specifically focused on developing models for mitigating Computer Fraud against Subscriber Identify Module (SIM) swapping and fake apps, as can be evidenced by SMSPROTECT the proposed model in Nigeria [1], which consists of an application to combat smishing using machine learning. Similarly, the model SMISHING DETECTOR [2] focused on the detection of smishing using machine learning, and the model in Turkey suggests a real-time system to detect and stop phishing [3].…”

Section: Introductionmentioning

confidence: 99%

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Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Melendez,

Noriega,

Tiznado

et al. 2024

Preprint

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This research aims to introduce a model, SectraBank, designed to mitigate cyber fraud attacks, specifically targeting SIM Swapping and Fake App schemes in mobile banking users. A quantitative methodology with an experimental design was meticulously employed to examine the rise of cyber fraud within mobile banking. The study involved a sample of 100 professionals with advanced knowledge in Information and Communication Technologies (ICTs). Surveys were utilized to measure awareness of threats and perceptions of security before and after the implementation of the proposed model. SectraBank leverages unique user identifiers such as fingerprint authentication and the International Mobile Equipment Identity (IMEI) number, in addition to geolocation and One Time Passwords (OTP) for validating electronic transactions in mobile banking. The effectiveness of the proposed model was notable, achieving a 96.1% efficacy rate in mitigating Fake App attacks and a 90.2% success rate in countering SIM Swapping attacks. These findings underscore the potential of SectraBank as a robust solution to enhance cybersecurity measures in the realm of mobile banking.

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Section: Of 31mentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Melendez,

Noriega,

Tiznado

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…SMS can be used to promote, increase customer interaction, and update users on various activities [3]. The low cost of sending text messages, the rapid increase in mobile phone subscribers, the ease of use, and the fact that SMS does not require internet connectivity to be delivered all contribute positively to the popularity of SMS for spammers [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Uncovering SMS Spam in Swahili Text Using Deep Learning Approaches

Mambina,

Ndibwile,

Uwimpuhwe

et al. 2024

IEEE Access

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In today's communications, Short Message Service (SMS) and Internet protocol-based messaging systems are the most widely used channels. These services are currently the target of an unprecedented number of threats due to their rising appeal. Some spammers have more nefarious motives, even though most spam stems from businesses seeking to promote their products. In recent years, as new security threats such as Smishing have emerged, the amount of SMS spam has experienced substantial growth. The scientific community has paid less attention to SMS spam than email spam despite the fact that both are extensively utilized. SMS spam presents extra processing hurdles. These include the use of lexical variants, SMS-like contractions, or sophisticated obfuscations, which degrade the performance of conventional filtering solutions. In this study, we investigate the efficacy of deep-learning models for filtering Swahili SMS spam based on linguistics and behavioral patterns using a real-world dataset from telecommunications companies in Tanzania. To validate the effectiveness of our models we subjected them to the UCI dataset of spam messages written in English. The models were trained and tested with 10 k-fold cross validation. The experimental results show that CNN-LSTM-LSTM hybrid model attained the highest accuracy of 99.98 on Swahili dataset while CNN-BiLSTM performed better on UCI dataset with an accuracy of 98.38.

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“…The primary data collection tool was a structured questionnaire, and the Statistical Package for Social Science (SPSS) was used to code and analyze the data. (Akande et al, 2023) introduces a mobile application designed to identify and thwart smishing attacks through a rule-based SMS service. The mobile application is equipped with a feature that intercepts incoming SMS messages to the smartphone.…”

mentioning

confidence: 99%

Banks Short Message Service Threats Notification System on Android Based Phone

Muhammad,

Liman,

Abah

et al. 2024

FJS

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This research is carried out for the development and implementation of an end-to-end encrypted Short Message Service (SMS) App, utilizing a hybrid cipher algorithm, driven by the notable insecurity observed in SMS communications on mobile devices. SMS is a widely used communication method, and the primary goal of this study is to create a system (App) for end-to-end encrypted SMS. Loss of phones is becoming vulnerable for threats, because we store vital records in android and these records are secret such that no other third party is required to see them, such as Bank SMS, Event Notification etc. Also this information can be compromised if android phone is been lost and found by the criminals. Kidnapping activity is serious case happening in northern part of Nigeria, so if a phone is being lost and discovered how financed he is (the device holder), there is any means of bank account compromisation as that would allow the bank to give some information about the account because of the registered SIM card inserted in android devices (phones). This system is aimed to secure Bank SMS by not allowing an unauthorized person to views the banks SMS, but all other SMS can be view. Whenever the Banks SMS is received by the Android phone, the system will encrypt it and can only be viewed if using correct decryption key, a notification will be sent to the owner other phone if more than two attempt to view the message is made, using a Hybrid Cipher...

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SMSPROTECT: An automatic smishing detection mobile application

Cited by 17 publications

References 10 publications

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Uncovering SMS Spam in Swahili Text Using Deep Learning Approaches

Banks Short Message Service Threats Notification System on Android Based Phone

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