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Solving the data imbalance problem in network intrusion detection : A MP-CVAE based method

In the area of network intrusion detection, harmful cyber-attacks are usually rare when compared to normal samples, which makes feature extraction and intrusion detection difficult. Increasing the amount of the anomalous samples through data generation can effectively relieve class imbalance, and thus significantly improve the classifier's performance. Variational Autoencoder (VAE) is an effective data generation model that assumes that the latent variables satisfy one prior distribution and creates new data by decoding the latent variables sampled from the distribution. This assumption is reasonable for unsu-pervised learning. However, when multiple types of attack data have obvious distribution differences, using the traditional VAE may cause various types of generated data to be mixed together. To solve this problem, we propose an improved generation model based on the conditional variational autoencoder, which encodes different types of samples into Gaussian distributions with significant difference to strengthen the discrimination between different classes in the data generation process. A series of experiments based on fitted data and real data, such as NSL-KDD, CIC-IDS2017, and CSE-CIC-IDS2018, demonstrate the effectiveness of this method.