Open Access

ARTICLE

Glowworm Optimization with Deep Learning Enabled Cybersecurity in Social Networks

by Ashit Kumar Dutta^1,*, Basit Qureshi², Yasser Albagory³, Majed Alsanea⁴, Anas Waleed AbulFaraj⁵, Abdul Rahaman Wahab Sait⁶

1 Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, 13713, Kingdom of Saudi Arabia
2 Department of Computer Science, Prince Sultan University, Riyadh, 11586, Kingdom of Saudi Arabia
3 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Kingdom of Saudi Arabia
4 Department of Computing, Arabeast Colleges, Riyadh, 11583, Kingdom of Saudi Arabia
5 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 23613, Kingdom of Saudi Arabia
6 Department of Archives and Communication, King Faisal University, Al Ahsa, Hofuf, 31982, Kingdom of Saudi Arabia

* Corresponding Author: Ashit Kumar Dutta. Email:

Intelligent Automation & Soft Computing 2022, 34(3), 2097-2110. https://doi.org/10.32604/iasc.2022.027500

Received 19 January 2022; Accepted 23 February 2022; Issue published 25 May 2022

Abstract

Recently, the exponential utilization of Internet has posed several cybersecurity issues in social networks. Particularly, cyberbulling becomes a common threat to users in real time environment. Automated detection and classification of cyberbullying in social networks become an essential task, which can be derived by the use of machine learning (ML) and deep learning (DL) approaches. Since the hyperparameters of the DL model are important for optimal outcomes, appropriate tuning strategy becomes important by the use of metaheuristic optimization algorithms. In this study, an effective glowworm swarm optimization (GSO) with deep neural network (DNN) model named EGSO-DNN is derived for cybersecurity in social networks. The proposed EGSO-DNN technique is mainly intended to identify the presence of cyberbullying on social networking sites. Besides, the EGSO-DNN technique involves different levels of pre-processing to transform the raw data into useful format. In addition, word2vec based feature extraction technique is applied to generate a set of feature vectors. Finally, the DNN model is used for the detection and classification of the DNN model where the hyperparameters of the DNN model are adjusted proficiently by the use of GSO algorithm. In order to ensure the supremacy of the EGSO-DNN technique, a series of simulations were carried out and the results are tested using benchmark datasets. The comparative analysis reported the improvements of the EGSO-DNN technique over the recent approaches maximum prec_n, reca_l, and F1_score of 0.9974, 0.9959, and 0.9966.

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Keywords

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