Ensemble Based Detection Model for DDoS Attacks in SDNs Using Advanced Feature Selection
International Conference on Signal Processing and Communication Systems(2024)
Abstract
Software-Defined Networking (SDN) enhances flexibility, scalability, and innovation by decoupling the control plane from the data plane, managed through streamlined controller operations. However, Distributed Denial of Service (DDoS) attacks pose significant cybersecurity threats to SDNs, disrupting services by flooding targeted systems with traffic from multiple sources. Real-time detection of these attacks remains challenging, as traditional methods often lack the ability to accurately identify complex attack patterns due to limited feature sets. To address this, we propose an ensemble-based model that combines three classifiers (SGD, EBM, and MLP) for effective DDoS attack detection and mitigation in SDNs. Our approach integrates a reliable feature selection methodology, leveraging Principal Component Analysis (PCA) to identify the most informative features for attack detection. This enhancement significantly improves the accuracy and efficiency of the ensemble model. Evaluated using the CIC-DDoS2019 and InSDN datasets, our model demonstrates substantial improvements in detection rates and computational efficiency. Results show that the proposed approach achieves 99% accuracy, underscoring its potential as a resilient solution for DDoS attack detection in SDNs.
MoreTranslated text
Key words
SDN,Feature Selection,Ensemble Learning,DDoS Attack,Detection,Mitigation
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined