LSTM-CGAN: Towards Generating Low-Rate DDoS Adversarial Samples for Blockchain-Based Wireless Network Detection Models
Low-rate Distributed DoS (LDDoS) attack is a complex large-scale attack behavior with strong time-domain characteristics in blockchain-based wireless network. Blockchain with Machine learning-based models, as promising ways, are taken to detect them and secure wireless network. However, researchers...
Main Authors: | Zengguang Liu, Xiaochun Yin |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9344707/ |
Similar Items
-
Spatiotemporal Scenario Generation of Traffic Flow Based on LSTM-GAN
by: Chao Wu, et al.
Published: (2020-01-01) -
An LSTM Based Generative Adversarial Architecture for Robotic Calligraphy Learning System
by: Fei Chao, et al.
Published: (2020-10-01) -
Generating and Measuring Similar Sentences Using Long Short-Term Memory and Generative Adversarial Networks
by: Zhiyao Liang, et al.
Published: (2021-01-01) -
Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN
by: Negin Hayatbini, et al.
Published: (2019-09-01) -
Design and Development of AD-CGAN: Conditional Generative Adversarial Networks for Anomaly Detection
by: Okwudili M. Ezeme, et al.
Published: (2020-01-01)