CML-IDS: Enhancing Intrusion Detection in SDN Through Collaborative Machine Learning
Author:
Affiliation:
1. Technical University of Darmstadt,Multimedia Communications Lab (KOM),Germany
2. Communication Networks, Technical University of Chemnitz,Germany
Funder
BMBF
German Research Foundation
CRC
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10327686/10327788/10327863.pdf?arnumber=10327863
Reference24 articles.
1. Host Bypassing: Let your GPU speak Ethernet
2. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
3. Improving DDoS Attack Detection Leveraging a Multi-aspect Ensemble Feature Selection
4. Ensemble-Based Online Machine Learning Algorithms for Network Intrusion Detection Systems Using Streaming Data
5. Programmable Switches for in-Networking Classification
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3. SSCL-IDS: Enhancing Generalization of Intrusion Detection with Self-Supervised Contrastive Learning;2024 IFIP Networking Conference (IFIP Networking);2024-06-03
4. HALIDS: a Hardware-Assisted Machine Learning IDS for in-Network Monitoring;2024 8th Network Traffic Measurement and Analysis Conference (TMA);2024-05-21
5. RDA: Residence Delay Aggregation for Time-Sensitive Networking;NOMS 2024-2024 IEEE Network Operations and Management Symposium;2024-05-06
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