Automated Spam Detection Using Sandpiper Optimization Algorithm-Based Feature Selection with the Machine Learning Model

References

• Y. V. Lakshmi, Y. S. Priyanka, A. Harika, N. Rajitha, and D. Bhargavi, “Machine learning based credit card fraud detection,” in 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2023, pp. 299–305.
   Google Scholar
• R. Karthick, and M. Sundararajan, “A novel 3-D-IC test architecture-a review,” Int. J. Eng. Technol., Vol. 7, no. 1, pp. 582–586, 2018.
   Google Scholar
• N. Nagarani, R. Karthick, M. S. Sophia, and M. B. Binda, “Self-attention based progressive generative adversarial network optimized with momentum search optimization algorithm for classification of brain tumor on MRI image,” Biomed. Signal Process. Control., Vol. 88, pp. 105597, 2024.
   Web of Science ®Google Scholar
• J. R. Mendez, T. R. Cotos-Yanez, and D. Ruano-Ordas, “A new semantic-based feature selection method for spam filtering,” Appl. Soft. Comput., Vol. 76, pp. 89–104, 2019.
   Web of Science ®Google Scholar
• N. Ahmed, R. Amin, H. Aldabbas, D. Koundal, B. Alouffi, and T. Shah, “Machine learning techniques for spam detection in email and IoT platforms: Analysis and research challenges,” Secur. Commun. Netw., Vol. 2022, pp. 1–19, 2022.
   Web of Science ®Google Scholar
• A. K. Singh, S. Bhushan, and S. Vij, “Filtering spam messages and mails using fuzzy C means algorithm,” in 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU). Ghaziabad: IEEE, 2019, pp. 1–5.
   Google Scholar
• R. Reka, R. Karthick, R. S. Ram, and G. Singh, “Multi head self-attention gated graph convolutional network based multi‑attack intrusion detection in MANET,” Comput. Secur., Vol. 136, pp. 103526, 2024.
   Web of Science ®Google Scholar
• C. Rajesh, P. Meenalochini, S. K. Kannaiah, and A. Bindu, “A hybrid control topology for cascaded H-bridge multilevel inverter to improve the power quality of smart grid connected system: NBO-RERNN approach,” Expert Syst. Appl., Vol. 238, pp. 122054, 2024.
   Web of Science ®Google Scholar
• A. P. Rodrigues, R. Fernandes, A. Shetty, K. Lakshmanna, and R. M. Shafi, “Real-time Twitter spam detection and sentiment analysis using machine learning and deep learning techniques,” Comput. Intell. Neurosci., Vol. 2022, pp. 5211949, 2022.
   PubMed Web of Science ®Google Scholar
• P. Meenalochini, and E. Sakthivel, “An efficient GBDTRSO control strategy for PV connected H-Bridge Nine Level MLI System with quasi-Z-source inverter,” Appl. Soft Comput., Vol. 113, pp. 108026, 2021.
   Web of Science ®Google Scholar
• A. Mohajer, M. S. Daliri, A. Mirzaei, A. Ziaeddini, M. Nabipour, and M. Bavaghar, “Heterogeneous computational resource allocation for NOMA: Toward green mobile edge-computing systems,” IEEE Trans. Services Comput., Vol. 16, no. 2, pp. 1225–38, 2022.
   Web of Science ®Google Scholar
• S. Dong, J. Zhan, W. Hu, A. Mohajer, M. Bavaghar, and A. Mirzaei, “Energy-efficient hierarchical resource allocation in uplink-downlink decoupled NOMA HetNets,” IEEE Trans. Netw. Serv. Manage, Vol. 20, no. 3, pp. 3380–95, 2023.
   Web of Science ®Google Scholar
• A. Mohajer, F. Sorouri, A. Mirzaei, A. Ziaeddini, K. J. Rad, and M. Bavaghar, “Energy-aware hierarchical resource management and backhaul traffic optimization in heterogeneous cellular networks,” IEEE Syst. J., Vol. 16, no. 4, pp. 5188–99, 2022.
   Web of Science ®Google Scholar
• S. Rapacz, P. Chołda, and M. Natkaniec, “A method for fast selection of machine-learning classifiers for spam filtering,” Electronics, Vol. 10, no. 17, pp. 2083–21, 2021.
   Web of Science ®Google Scholar
• S. Rao, A. K. Verma, and T. Bhatia, “Hybrid ensemble framework with self-attention mechanism for social spam detection on imbalanced data,” Expert. Syst. Appl., Vol. 217, pp. 119594, 2023.
   Web of Science ®Google Scholar
• K. L. Chiew, C. L. Tan, K. Wong, K. S. Yong, and W. K. Tiong, “A new hybrid ensemble feature selection framework for machine learning-based phishing detection system,” Inf. Sci., Vol. 484, pp. 153–66, 2019.
   Web of Science ®Google Scholar
• C. Wang, Q. Li, T. Y. Ren, X. H. Wang, and G. X. Guo, “High efficiency spam filtering: A manifold learning-based approach,” Math. Probl. Eng., Vol. 2021, pp. 1–7, 2021.
   Web of Science ®Google Scholar
• K. S. Mohsen, and W. S. Bhaya, “Attacks of social networks detection using multiple feature selections and dynamic machine learning,” Webology, Vol. 19, no. 2, pp. 952–66, 2022.
   Google Scholar
• Z. Zhang, M. Zhou, J. Wan, K. Lu, G. Chen, and H. Liao, “Spammer detection via ranking aggregation of group behavior,” Expert. Syst. Appl., Vol. 216, pp. 119454, 2023.
   Web of Science ®Google Scholar
• N. F. Othman, and W. I. S. W. Din, “YouTube spam detection framework using naïve Bayes and logistic regression,” Indonesian J. Electric. Eng. Comput. Sci., Vol. 14, no. 3, pp. 1508–17, 2019.
   Google Scholar
• V. D. P. Jasti, et al., “Relevant-based feature ranking (RBFR) method for text classification based on machine learning algorithm,” J. Nanomater., Vol. 2022, pp. 1–12, 2022.
   Web of Science ®Google Scholar
• G. S. Thejas, S. R. Joshi, S. S. Iyengar, N. R. Sunitha, and P. Badrinath, “Mini-batch normalized mutual information: a hybrid feature selection method,” IEEE. Access., Vol. 7, pp. 116875–85, 2019.
   Google Scholar
• M. B. Shishehgarkhaneh, M. Azizi, M. Basiri, and R. C. Moehler, “BIM-based resource tradeoff in project scheduling using fire hawk optimizer (FHO),” Buildings, Vol. 12, no. 9, pp. 1472, 2022.
   Web of Science ®Google Scholar
• T. S. Kavitha, and D. K. S. Prasad, “A novel method of compressive sensing MRI reconstruction based on sandpiper optimization algorithm (SPO) and mask region based convolution neural network (mask RCNN),” Multimed. Tools. Appl., Vol. 81, no. 22, pp. 31469–92, 2022.
   Web of Science ®Google Scholar
• P. Bhanupriya, and B. Ramachandran, “Imposition recognition in vehicular networks using hybrid offloading techniques with improved radial bias neural network,” Soft. Comput., Vol. 27, pp. 15425–41, 2023.
   Web of Science ®Google Scholar