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Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

Mesfer Al Duhayyim1, Haya Mesfer Alshahrani2, Fahd N. Al-Wesabi3, Mohammed Alamgeer4, Anwer Mustafa Hilal5,*, Mohammed Rizwanullah5

1 Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Saudi Arabia
3 Department of Computer Science, King Khalid University, Muhayel Aseer, Saudi Arabia & Faculty of Computer and IT, Sana’a University, Sana’a, Yemen
4 Department of Information Systems, King Khalid University, Muhayel Aseer, Saudi Arabia
5 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email: email

Computers, Materials & Continua 2022, 70(3), 6257-6270. https://doi.org/10.32604/cmc.2022.021212

Abstract

Cybersecurity encompasses various elements such as strategies, policies, processes, and techniques to accomplish availability, confidentiality, and integrity of resource processing, network, software, and data from attacks. In this scenario, the rising popularity of Online Social Networks (OSN) is under threat from spammers for which effective spam bot detection approaches should be developed. Earlier studies have developed different approaches for the detection of spam bots in OSN. But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning (DL) models needs to be explored. With this motivation, the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as SBD-HDL. The proposed SBD-HDL technique focuses on the detection of spam bots that exist in OSNs. The technique has different stages of operations such as pre-processing, classification, and parameter optimization. Besides, SBD-HDL technique hybridizes Graph Convolutional Network (GCN) with Recurrent Neural Network (RNN) model for spam bot classification process. In order to enhance the detection performance of GCN-RNN model, hyperparameters are tuned using Lion Optimization Algorithm (LOA). Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work, a first-of-its-kind in this domain. The experimental validation of the proposed SBD-HDL technique, conducted upon benchmark dataset, established the supremacy of the technique since it was validated under different measures.

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APA Style
Duhayyim, M.A., Alshahrani, H.M., Al-Wesabi, F.N., Alamgeer, M., Hilal, A.M. et al. (2022). Deep learning empowered cybersecurity spam bot detection for online social networks. Computers, Materials & Continua, 70(3), 6257-6270. https://doi.org/10.32604/cmc.2022.021212
Vancouver Style
Duhayyim MA, Alshahrani HM, Al-Wesabi FN, Alamgeer M, Hilal AM, Rizwanullah M. Deep learning empowered cybersecurity spam bot detection for online social networks. Comput Mater Contin. 2022;70(3):6257-6270 https://doi.org/10.32604/cmc.2022.021212
IEEE Style
M.A. Duhayyim, H.M. Alshahrani, F.N. Al-Wesabi, M. Alamgeer, A.M. Hilal, and M. Rizwanullah "Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks," Comput. Mater. Contin., vol. 70, no. 3, pp. 6257-6270. 2022. https://doi.org/10.32604/cmc.2022.021212



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