Toward a Reliable Evaluation of Machine Learning Schemes for Network-Based Intrusion Detection
Author:
Affiliation:
1. Pontifícia Universidade Católica do Paraná (PUCPR),Brazil
2. Technology Innovation Institute, Autonomous Robotics Researcher Center,Abu Dhabi,United Arab Emirates
Funder
National Council for Scientific and Technological Development
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/8548628/10145006/10145038.pdf?arnumber=10145038
Reference15 articles.
1. MAWILab
2. Synthetic attack data generation model applying generative adversarial network for intrusion detection
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4. Paradise: Real-Time, Generalized, and Distributed Provenance-Based Intrusion Detection;wu;IEEE Trans Dependable and Secure Computing,2022
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2. A Dynamic Network-based Intrusion Detection Model for Industrial Control Systems;2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom);2023-11-01
3. On the Robustness of ML-Based Network Intrusion Detection Systems: An Adversarial and Distribution Shift Perspective;Computers;2023-10-17
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