Towards a secure Software Defined Network with Adaptive Mitigation of DDoS attacks by Machine Learning Approaches
Author:
Affiliation:
1. Ponjesly College of Engineering,Department of Computer Science and Engineering
2. Sri Krishna College of Engineering and Technology,Department of Computer Science and Engineering
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9751777/9752467/09752607.pdf?arnumber=9752607
Reference23 articles.
1. A survey of DDoS attacking techniques and defence mechanisms in the IoT network
2. Study of detection method for spoofed IP against DDoS attacks
3. SAD-IoT: Security Analysis of DDoS Attacks in IoT Networks
4. An anomaly-based framework for mitigating effects of DDoS attacks using a third party auditor in cloud computing environments
5. TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification
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1. Artificial Intelligence and Quantum Synergies in Trust-Enhanced Consumer Applications for Software Defined Networks;IEEE Transactions on Consumer Electronics;2024-02
2. Detection of DDoS Attacks in SDN Using Machine Learning Approaches: A Review;Lecture Notes in Networks and Systems;2024
3. Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment;Network;2023-12-01
4. A novel DDoS detection and mitigation technique using hybrid machine learning model and redirect illegitimate traffic in SDN network;Cluster Computing;2023-10-17
5. DDoS Detection Using Hybrid Deep Neural Network Approaches;2023 IEEE 8th International Conference for Convergence in Technology (I2CT);2023-04-07
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