Boundary-Seeking GAN Approach to Improve Classification of Intrusion Detection Systems Based on Machine Learning Model
Author:
Affiliation:
1. Chaoyang University of Technology,Department of Information Management,Taichung,Taiwan,R.O.C.
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10035563/10035540/10035580.pdf?arnumber=10035580
Reference24 articles.
1. Evading Machine Learning Botnet Detection Models via Deep Reinforcement Learning
2. Intrusion Detection with Tree-Based Data Mining Classification Techniques by Using KDD
3. IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection;lin;Advances in Knowledge Discovery and Data Mining,2018
4. A Statistical Analysis on KDD Cup'99 Dataset for the Network Intrusion Detection System;kumar sunanda;Int Conf Adv Commun Netw,0
5. Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data;shi;IEEE Int Symp Signal Process Inf Technol ISSPIT,2019
Cited by 1 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. SR-IDS: A Novel Network Intrusion Detection System Based on Self-taught Learning and Representation Learning;Artificial Neural Networks and Machine Learning – ICANN 2023;2023
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