Nov 9, 2021 · We propose two separate deep learning algorithms to filter network traffic flows: Latent Layer Classification on a Variational Autoencoder (LLC-VAE), and Loss Based …

The continually evolving nature of DDoS attacks is addressed by the profound neural network nature of A Variational Autoencoder, which makes a VAE automatically learn new patterns in …

Two methods based on the ability of a Variational Autoencoder to learn useful latent representations from network traffic flows are proposed, to differentiate between normal and …

In this paper, we propose a new integrated model based on deep autoencoder (AE) for anomaly detection and feature extraction. Firstly, AE is trained based on normal network traffic and used...

Distributed Denial-of-Service (DDoS) defense mechanisms have been a significant research issue in network security. A wide variety of DDoS defense strategies ha

Nov 18, 2024 · Learn how to build a robust Network Intrusion Detection System using Variational Autoencoders for advanced anomaly detection and enhanced network security.

This study presents new method to detect DDOS attacks by using multistage auto-encoders based on Radial Basis Function (RBF). The input data which represented the DDoS features …

To build a Variational Autoencoder based Deep Neural Network that detects DDOS attacks with high precision and high recall. To design and build a Variational Autoencoder model which...

May 14, 2021 · Two methods based on the ability of Variational Autoencoders to learn latent representations from network traffic flows are proposed. The first method resorts to a classifier …

Although many distributed denial of service (DDoS) attacks detection algorithms have been proposed and even some of them have claimed high detection accuracy, D