Reference Hub

This research has been cited in:

Article
How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural NetworkInternational Journal of Information Management Data Insights10.1016/j.jjimei.2022.100147
Article
Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural NetworkSN Computer Science10.1007/s42979-022-01078-0
Article
A survey of phishing attack techniques, defence mechanisms and open research challengesEnterprise Information Systems10.1080/17517575.2021.1896786
Article
On-Device Smishing Classifier Resistant to Text Evasion AttackIEEE Access10.1109/ACCESS.2024.3349577
Article
An investigation in detection and mitigation of smishing using machine learning techniquesSocial Network Analysis and Mining10.1007/s13278-023-01142-4
Conference
Security Analysis and Threat Modelling of Mobile Banking Applications2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10307577
Conference
SMS Phishing and Mitigation Approaches2019 Twelfth International Conference on Contemporary Computing (IC3)10.1109/IC3.2019.8844920
Chapter
Phishing Attack Types and Mitigation: A SurveyData Science and Emerging Technologies10.1007/978-981-99-0741-0_10
Article
Phishing Classification Techniques: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2022.3166474
Article
Smishing Detector: A security model to detect smishing through SMS content analysis and URL behavior analysisFuture Generation Computer Systems10.1016/j.future.2020.03.021
Article
An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam DetectionApplied Computational Intelligence and Soft Computing10.1155/2023/6648970
Article
Classifying Swahili Smishing Attacks for Mobile Money Users: A Machine-Learning ApproachIEEE Access10.1109/ACCESS.2022.3196464
Article
A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English MessagesFuture Internet10.3390/fi12090156
Chapter
Spam Filtering of Mobile SMS Using CNN–LSTM Based Deep Learning ModelHybrid Intelligent Systems10.1007/978-3-030-96305-7_10
Conference
SMSDect: A Prediction Model for Smishing Attack Detection Using Machine Learning and Text AnalysisGLOBECOM 2023 - 2023 IEEE Global Communications Conference10.1109/GLOBECOM54140.2023.10437712
Article
A content and URL analysis‐based efficient approach to detect smishing SMS in intelligent systemsInternational Journal of Intelligent Systems10.1002/int.23035
Chapter
SMS Phishing Dataset for Machine Learning and Pattern RecognitionProceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022)10.1007/978-3-031-27524-1_57
Article
DSmishSMS-A System to Detect Smishing SMSNeural Computing and Applications10.1007/s00521-021-06305-y
Conference
Using Machine Learning for Efficient Smishing Detection2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)10.1109/ICCE-Taiwan58799.2023.10226901
Conference
COPS: A Compact On-Device Pipeline for Real-Time Smishing Detection2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)10.1109/CCNC51664.2024.10454635
Article
SM‐Detector: A security model based on BERT to detect SMiShing messages in mobile environmentsConcurrency and Computation: Practice and Experience10.1002/cpe.6452
Article
A Novel Approach to Detect Spam and Smishing SMS using Machine Learning TechniquesInternational Journal of E-Services and Mobile Applications10.4018/IJESMA.2020010102
Article
Exploring Consumers' Intention to Adopt Mobile Payment Systems in GhanaInternational Journal of E-Services and Mobile Applications10.4018/IJESMA.285547
Chapter
A Survey on Sentiment Analysis Techniques for TwitterData Mining Approaches for Big Data and Sentiment Analysis in Social Media10.4018/978-1-7998-8413-2.ch003
Chapter
A Novel Approach to Detect Spam and Smishing SMS using Machine Learning TechniquesResearch Anthology on Securing Mobile Technologies and Applications10.4018/978-1-7998-8545-0.ch014

Feature Based Approach for Detection of Smishing Messages in the Mobile Environment

Ankit Kumar Jain, B. B. Gupta

Source Title: Journal of Information Technology Research (JITR)12(2)

ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781522564751|DOI: 10.4018/JITR.2019040102

Cite Article Cite Article

MLA

Jain, Ankit Kumar, and B. B. Gupta. "Feature Based Approach for Detection of Smishing Messages in the Mobile Environment." JITR vol.12, no.2 2019: pp.17-35. http://doi.org/10.4018/JITR.2019040102

APA

Jain, A. K. & Gupta, B. B. (2019). Feature Based Approach for Detection of Smishing Messages in the Mobile Environment. Journal of Information Technology Research (JITR), 12(2), 17-35. http://doi.org/10.4018/JITR.2019040102

Chicago

Jain, Ankit Kumar, and B. B. Gupta. "Feature Based Approach for Detection of Smishing Messages in the Mobile Environment," Journal of Information Technology Research (JITR) 12, no.2: 17-35. http://doi.org/10.4018/JITR.2019040102

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

Smishing is a security attack that is performed by sending a fake message intending to steal personal credentials of mobile users. Nowadays, smishing attack becomes popular due to the massive growth of mobile users. The smishing message is very harmful since its target to financial benefits. In this article, the authors present a new feature-based approach to detect smishing messages in the mobile environment. This approach offers ten novel features that distinguish the fake messages from the ham messages. In this article, the authors have also identified the nineteen most suspicious keywords, which are used by the attacker to lure victims. This article has implemented these features on benchmarked dataset and applied numerous classification algorithms to judge the performance of the proposed approach. Experimental outcomes indicate that proposed approach can detect smishing messages with the 94.20% true positive rate and 98.74% overall accuracy. Furthermore, the proposed approach is very efficient for the detection of the zero hour attack.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

Feature Based Approach for Detection of Smishing Messages in the Mobile Environment

MLA

APA

Chicago

Export Reference

Abstract

Request Access