A Hybrid GCN and RNN Structure Based on Attention Mechanism for Text Classification

Gao, Lingchao; Wang, Jiakai; Pi, Zhixian; Zhang, Huaixun; Yang, Xiao; Huang, Peizhuo; Sun, Jun

doi:10.1088/1742-6596/1575/1/012130

Cited by 1 publication

(1 citation statement)

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“…It only needs the development of codec models in the graph method and softmax can be used on every node. Graph convolutional network [17] tend to recover the missing entities/relationships that could also execute the task of entity classification. The test result on few datasets shows that these network frameworks can improve the problems that arise from lost data in the knowledge base.…”

Section: Phase Ii: Data Classificationmentioning

confidence: 99%

Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

Duhayyim¹,

Alshahrani²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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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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Section: Phase Ii: Data Classificationmentioning

confidence: 99%