Efficient Malware Originated Traffic Classification by Using Generative Adversarial Networks-Reference-Cited by-同舟云学术

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Efficient Malware Originated Traffic Classification by Using Generative Adversarial Networks

Published:2020-07 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE Symposium on Computers and Communications (ISCC)
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Author:

Liu Zhicheng,Li Shuhao,Zhang Yongzheng,Yun Xiaochun,Cheng Zhenyu

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9213178/9219543/09219561.pdf?arnumber=9219561

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Cognitively Inspired Three-Way Decision Making and Bi-Level Evolutionary Optimization for Mobile Cybersecurity Threats Detection: A Case Study on Android Malware;Cognitive Computation;2024-09-06

2. Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques;Internet of Things;2024-07

3. Toward Efficient Neural Networks Through Predictor-Assisted NSGA-III for Anomaly Traffic Detection of IoT;IEEE Transactions on Cognitive Communications and Networking;2024-06

4. A Systematic Review on Generative Adversarial Network (GAN): Challenges and Future Directions;Archives of Computational Methods in Engineering;2024-05-14

5. Malware Classification Using Open Set Recognition and HTTP Protocol Requests;Lecture Notes in Computer Science;2024

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