Comparison of CNN and DNN Performance on Intrusion Detection System
Author:
Affiliation:
1. School of Electrical Engineering and Informatics Bandung Institute of Technology,Bandung,Indonesia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9915021/9915036/09915157.pdf?arnumber=9915157
Cited by 4 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
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2. CBF-IDS: Addressing Class Imbalance Using CNN-BiLSTM with Focal Loss in Network Intrusion Detection System;Applied Sciences;2023-10-24
3. Intrusive Detection Techniques Utilizing Machine Learning, Deep Learning, and Anomaly-based Approaches;2023 IEEE International Conference on Cryptography, Informatics, and Cybersecurity (ICoCICs);2023-08-22
4. A Comparative Study of Five Machine Learning Algorithms for Anomaly-based IDS;2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE);2022-12-22
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