Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection
This addresses network security challenges for cybersecurity applications, but is incremental as it builds on existing GAN frameworks.
The authors tackled network intrusion detection by developing a bidirectional GAN-based one-class classifier that directly identifies anomalous traffic without complex scoring, and their method outperformed similar generative approaches on the NSL-KDD dataset.
The network intrusion detection task is challenging because of the imbalanced and unlabeled nature of the dataset it operates on. Existing generative adversarial networks (GANs), are primarily used for creating synthetic samples from reals. They also have been proved successful in anomaly detection tasks. In our proposed method, we construct the trained encoder-discriminator as a one-class classifier based on Bidirectional GAN (Bi-GAN) for detecting anomalous traffic from normal traffic other than calculating expensive and complex anomaly scores or thresholds. Our experimental result illustrates that our proposed method is highly effective to be used in network intrusion detection tasks and outperforms other similar generative methods on the NSL-KDD dataset.