Challenges in Identifying Network Attacks Using Netflow Data
Author:
Affiliation:
1. The University of Exeter,Stocker Road,UK
2. Lancaster University,Bailrigg,UK
3. The University of Warwick
4. Intel Corporation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9685017/9685042/09685305.pdf?arnumber=9685305
Reference43 articles.
1. SD-WAN: An Open-Source Implementation for Enterprise Networking Services
2. Go With the Flow: Clustering Dynamically-Defined NetFlow Features for Network Intrusion Detection with DynIDS
3. NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems
4. DDoS never dies? An IXP perspective on DDoS amplification attacks;daniel;Passive and Active Measurement,2021
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1. An empirical study of reflection attacks using NetFlow data;Cybersecurity;2024-07-01
2. SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection;2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P);2023-07
3. MalTDec3: Malicious Traffic Detection in a Network using 3 Layer Inspection;2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT);2022-07
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