Jul 3, 2019 � In this paper, we propose a novel deep neural network-based detection mechanism that uses feed-forward back-propagation for accurately discovering multiple�...
Jul 3, 2019 � A Deep Learning Approach to Detection and Mitigation of Distributed Denial of Service Attacks in High Availability Intelligent Transport Systems.
The proposed neural network architecture can identify and use the most relevant high level features of packet flows with an accuracy of 98% on the state-of-the-�...
Jul 3, 2019 � In this paper, we propose a deep neural network architecture, consisting of seven hidden layers, and is based on feed-forward back-propagation�...
This paper proposes an effective detection technique against DDoS attack in SDN control plane and data plane.
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Distributed Denial of Service Attack Detection using Deep Learning ...
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The reporting results show that RNN based Stacked-LSTM Deep Learning approach produces the best results in detecting Portmap DDoS attack.
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In this work, we consider three SDN datasets for classifying DDoS attacks. We use three deep learning modules namely Multi-Layer Perceptron (MLP), Convolutional�...
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A "Long Short-Term Memory (LSTM)" based model is created in this study to identify DDoS threats on a sample of network traffic packets.
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Aug 16, 2024 � This paper introduces a novel deep learning-based intrusion detection system, specifically designed for deployment at either the Cloud or Fog level in the IoT�...
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Sep 11, 2023 � This project presents a novel deep learning-based approach for detecting DDoS attacks in network traffic using the industry-recognized DDoS evaluation dataset.
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