$\text{C}^{2}\text{BNVAE}$: Dual-Conditional Deep Generation of Network Traffic Data for Network Intrusion Detection System Balancing

TL;DR

This paper introduces $ ext{C}^{2} ext{BNVAE}$, a dual-conditional generative model that produces balanced, realistic network traffic data to enhance intrusion detection systems, especially for rare and novel attack types.

Contribution

It presents a novel dual-conditional VAE with Conditional Batch Normalization for generating category-specific network traffic data, improving class balance and detection accuracy.

Findings

Effective in balancing data distribution on NSL-KDD dataset

Improves NIDS detection performance for rare attacks

Lower computational overhead than baseline methods

Abstract

Network Intrusion Detection Systems (NIDS) face challenges due to class imbalance, affecting their ability to detect novel and rare attacks. This paper proposes a Dual-Conditional Batch Normalization Variational Autoencoder ($\text{C}^{2}\text{BNVAE}$) for generating balanced and labeled network traffic data. $\text{C}^{2}\text{BNVAE}$ improves the model's adaptability to different data categories and generates realistic category-specific data by incorporating Conditional Batch Normalization (CBN) into the Conditional Variational Autoencoder (CVAE). Experiments on the NSL-KDD dataset show the potential of $\text{C}^{2}\text{BNVAE}$ in addressing imbalance and improving NIDS performance with lower computational overhead compared to some baselines.