A Comparative Analysis of Deep Learning Models for Smishing Detection in SMS Message
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Abstract
Smishing (SMS phishing) is a cyber threat that is growing rapidly, and it's a tactic in which attackers use SMS messages to deceive users and trick them into revealing their private information or unintentionally installing harmful applications. As mobile devices are used everywhere, detecting smishing messages has become a very important yet difficult task in cybersecurity. In the present study, the authors conduct a comparative analysis of several deep learning models, namely, the Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi LSTM), Convolutional Neural Network (CNN), and a combination of CNN-LSTM, for the detection of smishing. The experiments are conducted on publicly available SMS datasets, and performance is evaluated using accuracy, precision, recall, F1 Score, and a confusion matrix. The findings indicate that deep learning-based approaches yield significantly better results than traditional methods, with hybrid architectures leading in overall performance.
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Shaikh Aqsa, Shaikh Mariya and Srivaramangai R., “Smishing Detection: Combating SMS Phishing Attacks by Utilizing Machine-Learning Algorithms”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075 (Online), Volume-14 Issue-5, April 2025, 6 pages DOI: https://doi.org/10.35940/ijitee.D1068.14050425
Shaikh Aqsa, Shaikh Mariya and Srivaramangai R., “Comparative Analysis of Machine Learning Models for Smishing Message Detection”, International Journal of Science and Research (IJSR), ISSN: 2319-7064, Impact Factor 2024: 7.101, 7 pages. SR251223103916 DOI: https://dx.doi.org/10.21275/SR251223103916
Daniel Timko, Daniel Hernandez Castillo, Muhammad Lutfor Rahman, “A Quantitative Study of SMS Phishing Detection,” Unpublished Manuscript (arXiv Preprint), 2024, 16 pages, DOI: https://doi.org/10.48550/arXiv.2311.06911
Ameen R. Mahmood, Sarab M. Hameed, “A Smishing Detection Method Based on SMS Contents Analysis and URL Inspection Using Google Engine and VirusTotal,” Iraqi Journal of Science, vol. 64, no. 10, 2023, 16 pages, DOI: http://doi.org/10.24996/ijs.2023.64.10.41
Ankit Kumar Jain, Sumit Kumar Yadav, Neelam Choudhary, “A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques,” International Journal of E-Services and Mobile Applications, vol. 12, no. 1, January-March 2020, 21 pages, DOI: https://doi.org/10.4018/IJESMA.2020010102
Sharif Omar, Mohammed Moshiul Hoque, A. S. M. Kayes, Raza Nowrozy, and Iqbal H. Sarker, “Detecting Suspicious Texts using Machine Learning Techniques,” Applied Sciences, vol. 10, no. 18, 2022, 23 pages, DOI: https://doi.org/10.3390/app10186527
Sanjukta Mohanty, Sourav Nanda, Rupayan Rout, Arpan Kumar, Vansam Agrawal, Arup Abhinna Acharya, Namita Panda, “Detection of Cyber Threats from Suspicious URLs Using Multi-Classification Approach” ResearchGate / Book Chapter, 2024, 14 pages, DOI: http://doi.org/10.4018/979-8-3693-1186-8.ch007
Anjali Shinde, Essa Q. Shahra, Shadi Basurra, Faisal Saeed, Abdulrahman A. AlSewari, and Waheb A. Jabbar, "SMS Scam Detection Application Based on Optical Character Recognition for Image Data Using Unsupervised and Deep Semi-Supervised Learning," Sensors 2024 19 pages DOI: https://doi.org/10.3390/s24186084
Daniel Schlette, Marco Caselli, and Gunther Pernul, “A Comparative Study on Cyber Threat Intelligence: The Security Incident Response Perspective,” IEEE Communications Surveys & Tutorials, 2021, DOI: http://doi.org/10.1109/COMST.2021.3117338
Christophe Chong, Daniel Liu (Stanford), and Wonhong Lee (Neustar), “Malicious URL Detection”, Unspecified publication, 4 pages, https://cs229.stanford.edu/proj2012/ChongLiu MaliciousURLDetection.pdf
Ms Shilpi Jain, Dr Madhur Jain, Ridhi Kalia, Divyansh Rampal, “A Comprehensive Model for Spam Detection and Phishing Link Detection,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10, no. 3, 2024, 5 pages, DOI: http://doi.org/10.32628/CSEIT24103109
Muskaan V. Jaiswal and Anjali B. Raut, “Detecting and Blocking of Malicious URL,” International Journal of Science and Research (IJSR), vol. 10, no. 6, 2021, 3 pages,DOI: http://doi.org/10.21275/SR21610230148
Malak Aljabri; Hanan S. Altamimi, Shahd A. Albelali, Maimunah Al Harbi, Haya T. Alhuraib, Najd K. Alotaibi, "Detecting Malicious URLs Detection Using Machine Learning Techniques: Review and Research Directions," IEEE Access, vol. 10, 2022, 23 pages, DOI: http://doi.org/10.1109/ACCESS.2022.3222307
Maruf A. Tamal, Md K. Islam, Touhid Bhuiyan, Abdus Sattar, Nayem Uddin Prince, “Unveiling Suspicious Phishing Attacks: Enhancing Detection with an Optimal Feature Vectorization Algorithm and Supervised Machine Learning,” Frontiers in Computer Science, vol. 6, no. 1428013, 2024, 16 pages, DOI: http://doi.org/10.3389/fcomp.2024.1428013
Amar Palwankar, Rifah Solkar, Afiya Borkar, Shreya Khedaskar, and Pranali Shingare, “Malicious Link Detection System,” International Research Journal of Engineering and Technology (IRJET), vol. 9, no. 11, 2022, 5 pages,
https://www.irjet.net/archives/V9/i11/IRJET V9I1165.pdf
Shiyun Li and Omar Dib, "Enhancing Online Security: A Novel Machine Learning Framework for Robust Detection of Known and Unknown Malicious URLs," Journal of Theoretical and Applied Electronic Commerce Research, vol. 19, no. 4, 2024, 42 pages, DOI: https://doi.org/10.3390/jtaer19040141
Yuan Jianting, Chen Guanxin, Tian Shengwei, Pei Xinjun, "Malicious URL Detection Based on a Parallel Neural Joint Model," IEEE Access, vol. 9, 2021, 9 pages, DOI: http://doi.org/10.1109/ACCESS.2021.3049625
Cho Do Xuan, Hoa Dinh Nguyen, Tisenko Victor Nikolaevich, "Malicious URL Detection Based on Machine Learning," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 11, no. 1, 2020, 6 pages,
DOI: http://dx.doi.org/10.14569/IJACSA.2020.0110119
Salvi Siddhi Ravindra, Shah Juhi Sanjay, Shaikh Nausheenbanu Ahmed Gulzar, Khodke Pallavi, " Phishing Website Detection Based on URL," IJSRCSEIT, vol. 7, no. 3, 2021, 6 pages, DOI: https://doi.org/10.32628/CSEIT2173124
Mohd Shoaib, Mohammad Sarosh Umar, " An investigation in detection and mitigation of smishing using machine learning techniques," Springer Nature Link vol 13, article 135, 2023, 15 pages, DOI:https://doi.org/10.1007/s13278-023-01142-4
Nuria Reyes-Dorta, Pino Caballero-Gil, Carlos Rosa-Remedios, "Detection of Malicious URLs Using Machine Learning," Wireless Networks, vol. 30, 2024, 18 pages, DOI: https://doi.org/10.1007/s11276-024-03700-w
Gunikhan Sonowal, "Detecting Phishing SMS Based on Multiple Correlation Algorithms," SN Computer Science, vol. 1, no. 361, 2020, 9 pages, DOI: https://doi.org/10.1007/s42979-020-00377-8
Hwabin Lee, Sua Jeong, Seogyeong Cho, and Eunjung Choi, "Visualisation Technology and Deep Learning for Multilingual Spam Message Detection," Electronics, 2023, Vol. 12, Article 582, 17 pages, DOI: https://doi.org/10.3390/electronics12030582
Tasfia Tabassum, Md. Mahbubul Alam, Md. Sabbir Ejaz, Mohammad Kamrul Hasan, “A Review on Malicious URLs Detection Using Machine Learning Methods," Journal of Engineering Research and Reports, vol. 25, no. 12, 2023, 13 pages, DOI: http://doi.org/10.9734/JERR/2023/v25i121042
Fuad A. Ghaleb, Mohammed Alsaedi, Faisal Saeed, Jawad Ahmad, Mohammed Alasli, "Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning," Sensors, vol. 22, no. 9, 2022, 19 pages, DOI: https://doi.org/10.3390/s22093373
Suparna Das Gupta et al., "SMS Spam Detection Using Machine Learning," Journal of Physics: Conference Series, vol. 1797, no. 1, 2021, 6 pages, DOI: http://doi.org/10.1088/1742-6596/1797/1/012017
Prof. Amar Palwankar, Afiya Borkar, Pranali Shingare, Rifah Solkar, Shreya Khedaskar, “Suspicious Link Detection Using AI," International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), vol. 3, no. 3, 2023, 8 pages, DOI: http://doi.org/10.48175/IJARSCT-9171
Bollam Pragna, M. Rama Bai, Spam Detection using NLP Techniques. (2019). In International Journal of Recent Technology and Engineering (Vol. 8, Issue 2S11, pp. 2423–2426). DOI: https://doi.org/10.35940/ijrte.b1280.0982s1119
Haritha Rajeev, Midhun Chakkravarthy (2023). Detection of Malware using Phishing Alarm. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 3, Issue 4, pp. 1–4). DOI: https://doi.org/10.54105/ijainn.a1077.124123
Manish Tiwari, Tripti Arjariya (2021). A Phishing URL Classification Technique using Machine Learning Approach. In International Journal of Innovative Technology and Exploring Engineering (Vol. 10, Issue 3, pp. 73–79). DOI: https://doi.org/10.35940/ijitee.c8338.0110321