Autoencoder-Boosted Lightweight Dense Net for Dimensionality Reduction and DOS Attack Classification in WSN
Keywords:
Deep learning, DoS attacks, Lightweight DenseNet, AutoencoderAbstract
Wireless Sensor Networks (WSNs) are liable to Denial of Service (DoS) attacks, which can be easily executed in this context. This study presents a comparative analysis of five prominent deep learning architectures, namely AlexNet, VGGNet, ResNet, DenseNet, and Lightweight DenseNet, for their efficacy in classifying Denial of Service (DoS) attacks in Wireless Sensor Networks (WSNs). The evaluation is conducted using labeled instances of different types of DoS attacks from the WSN-DS and IOTID20 datasets. Various evaluation metrics including F1-score, recall, precision and accuracy computational efficiency are employed to discern the suitability of these architectures for real-time WSN applications. Experimental results from training and testing on the WSN-DS and IOTID20 datasets provide insights into the performance of each architecture, aiding in the selection of optimal models for DoS attack classification in WSNs.
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