Spotted hyena optimizer with deep learning driven cybersecurity for social networks
Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech. Online provocation, abuses, and attacks are widely termed cyberbullying (CB). The massive quantity of user generated content makes it difficult to recognize CB. Current advancements...
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| المؤلفون الرئيسيون: | , , , , , , , |
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| التنسيق: | مقال |
| اللغة: | English English |
| منشور في: |
Tech Science Press
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://irep.iium.edu.my/101889/19/101889_Spotted%20hyena%20optimizer%20with%20deep%20learning%20driven%20cybersecurity%20for%20social%20networks.pdf http://irep.iium.edu.my/101889/13/101889_Spotted%20hyena%20optimizer%20with%20deep%20learning%20driven%20cybersecurity%20for%20social%20networks_Scopus.pdf http://irep.iium.edu.my/101889/ http://doi.org/10.32604/csse.2023.031181 |
| الوسوم: |
إضافة وسم
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| الملخص: | Recent developments on Internet and social networking have led to the
growth of aggressive language and hate speech. Online provocation, abuses, and
attacks are widely termed cyberbullying (CB). The massive quantity of user generated content makes it difficult to recognize CB. Current advancements in
machine learning (ML), deep learning (DL), and natural language processing
(NLP) tools enable to detect and classify CB in social networks. In this view, this
study introduces a spotted hyena optimizer with deep learning driven cybersecurity (SHODLCS) model for OSN. The presented SHODLCS model intends to
accomplish cybersecurity from the identification of CB in the OSN. For achieving
this, the SHODLCS model involves data pre-processing and TF-IDF based feature extraction. In addition, the cascaded recurrent neural network (CRNN) model
is applied for the identification and classification of CB. Finally, the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN
model and thereby results in enhanced classifier performance. The experimental
validation of the SHODLCS model on the benchmark dataset portrayed the better
outcomes of the SHODLCS model over the recent approaches. |
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