Performance Comparison of Recurrent Neural Networks for Metamorphic Malware

Mittal, Shubh; Chawla, Tisha; Jajoo, Jay; Bansal, Muskan; Parashar, Harsha; D, Ruby

doi:10.1109/icdsns58469.2023.10245978

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2024

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Cited by 2 publications

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A Robust Feature Integration for Multiclass Metamorphic Malware Detection in IoT Network

Chawla,

Rahman,

Pal

et al. 2024

2024 16th International Conference on COMmunication Systems &Amp; NETworkS (COMSNETS)

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A Robust Feature Integration for Multiclass Metamorphic Malware Detection in IoT Network

Chawla,

Rahman,

Pal

et al. 2024

2024 16th International Conference on COMmunication Systems &Amp; NETworkS (COMSNETS)

View full text Add to dashboard Cite

Optimizing hybrid neural networks for precise COVID-19 mRNA vaccine degradation prediction

Soon,

Abdullah,

Nishizaki

et al. 2024

Int. j. adv. appl. sci.

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Conventional hybrid models often miss an essential factor that can lead to less effective performance: intrinsic sequence dependence when combining various neural network (NN) architectures. This study addresses this issue by highlighting the importance of sequence hybridization in NN architecture integration, aiming to improve model effectiveness. It combines NN layers—dense, long short-term memory (LSTM), and gated recurrent unit (GRU)—using the Keras Sequential API for defining the architecture. To provide better context, bidirectional LSTM (BiLSTM) and bidirectional GRU (BiGRU) replace their unidirectional counterparts, enhancing the models through bidirectional structures. Out of 25 NN models tested, 18 four-layer hybrid NN models consist of one-quarter dense layer and the rest BiLSTM and BiGRU layers. These hybrid NN models undergo supervised learning regression analysis, with mean column-wise root mean square error (MCRMSE) as the performance metric. The results show that each hybrid NN model produces unique outcomes based on its specific hybrid sequence. The Hybrid_LGSS model performs better than existing three-layer BiLSTM networks in predictive accuracy and shows lower overfitting (MCRMSEs of 0.0749 and 0.0767 for training and validation, respectively). This indicates that the optimal hybridization sequence is crucial for achieving a balance between performance and simplicity. In summary, this research could help vaccinologists develop better mRNA vaccines and provide data analysts with new insights for improvement.

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Performance Comparison of Recurrent Neural Networks for Metamorphic Malware

Cited by 2 publications

References 20 publications

A Robust Feature Integration for Multiclass Metamorphic Malware Detection in IoT Network

A Robust Feature Integration for Multiclass Metamorphic Malware Detection in IoT Network

Optimizing hybrid neural networks for precise COVID-19 mRNA vaccine degradation prediction

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