CNN-BiLSTM Cloud Intrusion Detection Method Based on Contractive Auto-Encoder Feature Enhancement-Reference-Cited by-同舟云学术

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CNN-BiLSTM Cloud Intrusion Detection Method Based on Contractive Auto-Encoder Feature Enhancement

Published:2024-03-29 Issue: Volume: Page:2265-2270
ISSN:
Container-title:2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)
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Author:

Zhang Shuya¹,Jin Tong¹,Zhang Gongxuan¹

Affiliation:

1. Nanjing University of Science and Technology,Nanjing,China

Funder

National Natural Science Foundation of China (NSFC)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10579949/10579951/10581444.pdf?arnumber=10581444

Reference11 articles.

1. BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

2. A novel statistical analysis and autoencoder driven intelligent intrusion detection approach

3. Nonlinear dimensionality reduction for intrusion detection using auto-encoder bottleneck features

4. Hybrid Intelligent Intrusion Detection Scheme

5. Network Anomaly Detection with Stochastically Improved Autoencoder Based Models

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