Comparative Analysis of LSTM-Based Variant Models for Detecting Attacks in IoT Networks

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ID: 312685
2025
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Abstract
Internet of Things (IoT) networks have established unparalleled connection possibilities and convenient features and have set new challenges associated with dubious security barriers and potential attacks. This study evaluates attack detection performance of LSTM-Based Variant models namely LSTM, DeepBiLSTM and BLSTM recurrent neural networks by bringing up experiment analysis of the IoT networks. We evaluate the three models using benchmark IoT dataset in terms of detection accuracy, precision, recall and F1 measure. The experiment results indicate that the three LSTM-based models, LSTM, BiLSTM, and DeepBiLSTM, demonstrate a high level of performance, regardless of the batch size (32, 64, 128, 256, and 512). DeepBiLSTM has a little better overall performance, which validates its soundness and suitability towards scale in detecting attacks in large IoT networks.
Reference Key
imported_1777056025_69ebb9194d155 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shahid Mehmood
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
10.56979/1001/2025/1169
URL
https://jcbi.org/index.php/Main/article/view/1169
Keywords Keywords not found

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