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- Vol. 6 Issue 2
- Article
SMS Spam Detection System Using Hybrid CNN-BiLSTM with Explainable AI and Real-Time Android Integration
Authors
1 Assistant Professor Department of Information Technology. Er. Perumal Manimekalai College of Engineering. Hosur, Tamil Nadu, India. 2 3 4 Department of Information Technology Er.Perumal Manimekalai College of Engineering Hosur Tamil Nadu, India.
Article Information
- Published In
- Volume 6, Issue 2 (2026)
- Published Online
- March-April 2026
- Pages
- 411-415
- Article Type
- Original Article
References
1. Z. Akhtar and T. K. Das, ”Smishing detection using deep learning,” IEEE Access, vol. 9, pp. 62512–62524, 2021.
2. A. R. Shanmugasundaram et al., ”Machine Learning Models for SMS Spam Detection,” Proc. of the 2020 International Conference
on Com- puting, Communication and Networking Technologies (ICCCNT), IEEE, 2020, pp. 1-6.
3. S. Roy, R. Sinha, and V. R. Sharma, ”Spam detection in SMS commu- nications using Recurrent Neural Networks,” Journal of
Cybersecurity and Privacy, vol. 2, no. 1, pp. 43-57, 2022.
4. J. Smith and T. Doe,” Limitations of Naive Bayes in NLP contexts,” ACM Transactions on Information Systems, vol. 36, no. 4, 2018.
5. L. Chen, H. Wang, and G. Li, ”An optimization approach for SVM- based text classification,” Springer Neural Computing and
Applications, vol. 31, pp. 2481-2495, 2019.
6. P. Gupta, ”Textual analysis using Convolutional Neural Networks for malicious SMS identification,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 42, no. 9, pp. 2210-2223, 2020.
7. M. Hassan,”Bidirectional LSTMs for complex sequential pattern recog- nition in text,” IEEE/ACM Transactions on Networking, vol.
27, no. 3, 2019.
8. A. Kumar, V. Singh, and R. Singh,”Spam Detection Using Deep Learning: A Hybrid Approach,” IEEE Access, vol. 9, pp. 87634-
87645, 2021.
9. Y. Wang, S. Zhang, and X. Liu,”Explainable AI in Cybersecurity: A Review,” IEEE Security & Privacy, vol. 20, no. 2, pp. 40-48,
2022.
10. D. Patel and K. Shah,”Deploying deep learning models on mobile constraints for real-time inference,” ACM Mobile HCI, 2021, pp.
112- 120.
11. T. K. Sharma,” Federated Learning for Mobile Malware Detection,” IEEE Communications Magazine, vol. 60, no. 4, 2022.
12. H. Lee,” Phishing mitigation in 5G telecommunication networks,” Springer Security Informatics, vol. 12, no. 1, 2023
2. A. R. Shanmugasundaram et al., ”Machine Learning Models for SMS Spam Detection,” Proc. of the 2020 International Conference
on Com- puting, Communication and Networking Technologies (ICCCNT), IEEE, 2020, pp. 1-6.
3. S. Roy, R. Sinha, and V. R. Sharma, ”Spam detection in SMS commu- nications using Recurrent Neural Networks,” Journal of
Cybersecurity and Privacy, vol. 2, no. 1, pp. 43-57, 2022.
4. J. Smith and T. Doe,” Limitations of Naive Bayes in NLP contexts,” ACM Transactions on Information Systems, vol. 36, no. 4, 2018.
5. L. Chen, H. Wang, and G. Li, ”An optimization approach for SVM- based text classification,” Springer Neural Computing and
Applications, vol. 31, pp. 2481-2495, 2019.
6. P. Gupta, ”Textual analysis using Convolutional Neural Networks for malicious SMS identification,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 42, no. 9, pp. 2210-2223, 2020.
7. M. Hassan,”Bidirectional LSTMs for complex sequential pattern recog- nition in text,” IEEE/ACM Transactions on Networking, vol.
27, no. 3, 2019.
8. A. Kumar, V. Singh, and R. Singh,”Spam Detection Using Deep Learning: A Hybrid Approach,” IEEE Access, vol. 9, pp. 87634-
87645, 2021.
9. Y. Wang, S. Zhang, and X. Liu,”Explainable AI in Cybersecurity: A Review,” IEEE Security & Privacy, vol. 20, no. 2, pp. 40-48,
2022.
10. D. Patel and K. Shah,”Deploying deep learning models on mobile constraints for real-time inference,” ACM Mobile HCI, 2021, pp.
112- 120.
11. T. K. Sharma,” Federated Learning for Mobile Malware Detection,” IEEE Communications Magazine, vol. 60, no. 4, 2022.
12. H. Lee,” Phishing mitigation in 5G telecommunication networks,” Springer Security Informatics, vol. 12, no. 1, 2023
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