Network Intrusion Detection Based on Convolutional Recurrent Neural Network, Random Forest, and Federated Learning

Abstract

This paper presents a novel network intrusion detection framework that combines convolutional recurrent neural networks (CRNN) and random forest (RF) models within a federated learning setting. The proposed approach aims to address the challenges of data privacy, computational efficiency, and model generalization in traditional network intrusion detection methods. By leveraging the spatial feature extraction capabilities of CRNN and the feature selection and noise reduction properties of RF, the framework enhances the accuracy and robustness of attack detection. The integration of federated learning enables collaborative model training without compromising data privacy. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed method compared to state-of-the-art techniques, achieving high performance metrics such as accuracy, precision, recall, F1 score, and AUC. The proposed framework offers a promising solution for secure and efficient network intrusion detection in real-world scenarios, contributing to the advancement of cybersecurity practices.

Keywords

• federated learning framework
• convolutional recurrent neural networks
• network security enhancement
• temporal data processing
• random forest integration
• feature selection optimization