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Hybrid Deep Learning for Intrusion Detection by Mohamad Mbodji is a document available to read on EtoBox.

What is Hybrid Deep Learning for Intrusion Detection about?

This document presents a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Weight-Dropped Long Short-Term Memory (WDLSTM) networks for efficient intrusion detection in big data environments. The proposed model addresses challenges in recognizing unknown attacks and responding in real-time, demonstrating satisfactory performance on the UNSW-NB15 dataset compared to traditional methods. The paper outlines the model

Author

Mohamad Mbodji

Language

EN