ARCHIVES

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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.
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Cybersecurity and Privacy, vol. 2, no. 1, pp. 43-57, 2022.
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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,
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10. D. Patel and K. Shah,”Deploying deep learning models on mobile constraints for real-time inference,” ACM Mobile HCI, 2021, pp.
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12. H. Lee,” Phishing mitigation in 5G telecommunication networks,” Springer Security Informatics, vol. 12, no. 1, 2023