Feasibility Evaluation of Compact Flow Features for Real-time DDoS Attacks Classifications
Author:
Affiliation:
1. Institut Teknologi Telkom Purwokerto,Department of Informatics,Purwokerto,Indonesia
2. Research Center for Data and Information Sciences, National Research and Innovation Agency,Bandung,Indonesia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9994277/9994285/09994323.pdf?arnumber=9994323
Reference34 articles.
1. A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning
2. Robust Self-Protection Against Application-Layer (D)DoS Attacks in SDN Environment
3. Intelligent DDoS Detection in Botnet Combined with Packet-Level Features under SDN
4. A flexible SDN-based framework for slow-rate DDoS attack mitigation by using deep reinforcement learning
5. Machine-learning-assisted DDoS attack detection with P4 language
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1. Evaluating Drill-Down DDoS Destination Detection;2024 IEEE 49th Conference on Local Computer Networks (LCN);2024-10-08
2. Classification of DDoS Attacks Using Ensemble Learning;2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT);2024-03-15
3. DDoS Detection and Defense Based on PSO Optimized LSTM Neural Network;2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI);2023-12-01
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