Summary of Convolutional Neural Networks and Mixture Of Experts For Intrusion Detection in 5g Networks and Beyond, by Loukas Ilias et al.
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond
by Loukas Ilias, George Doukas, Vangelis Lamprou, Christos Ntanos, Dimitris Askounis
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, the authors propose a novel approach for identifying malicious traffic in 6G/NextG networks using Mixture of Experts (MoE) and convolutional neural network (CNN) layers. The existing studies on intrusion detection tasks rely on shallow machine learning classifiers or static deep neural networks, which are limited in their representation power and efficiency. To overcome these limitations, the authors integrate MoE with CNN to leverage the strengths of both models. They use network traffic data and convert the 1D array of features into a 2D matrix, then pass it through CNN layers followed by batch normalization and max pooling layers. The resulting representation vector is fed into a sparsely gated MoE layer that assigns weights to the output of each expert. Experiments on the 5G-NIDD dataset show that their proposed approach achieves a weighted F1-score of up to 99.95%, comparable to existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to detect malicious traffic in 6G/NextG networks using Artificial Intelligence. The authors use special kinds of neural networks called convolutional neural networks (CNNs) and mixture of experts (MoE) to help identify bad traffic. They show that this approach is better than what other researchers have done before because it can learn from the data more effectively. |
Keywords
» Artificial intelligence » Batch normalization » Cnn » F1 score » Machine learning » Mixture of experts » Neural network
