论文标题
培训基于双向GAN的单级分类器进行网络入侵检测
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection
论文作者
论文摘要
由于其运行的数据集的不平衡和未标记的性质，网络入侵检测任务具有挑战性。现有的生成对抗网络（GAN）主要用于创建来自REAL的合成样本。在异常检测任务中，它们也被证明是成功的。在我们提出的方法中，我们基于双向GAN（BI-GAN）来构建经过训练的编码器 - 分歧剂作为单级分类器，用于检测来自正常流量的异常流量，而不是计算昂贵且复杂的异常分数或阈值。我们的实验结果表明，我们提出的方法非常有效，可以在网络入侵检测任务中使用，并且在NSL-KDD数据集上胜过其他类似的生成方法。
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.
