IGAN-IDS: an Imbalanced Generative Adversarial Network Towards Intrusion Detection System in Ad-Hoc Networks.

With the emergence of ever-advancing network threats, the guarantee of system security becomes increasingly crucial, especially in the dynamic and decentralized ad-hoc networks. One essential part of cybersecurity is intrusion detection, which identifies anomalous activities according to traffic patterns. However, the class-imbalanced data have caused a challenging problem where the number of abnormal samples is significantly lower than that of the normal ones. This class imbalance problem confines the performance of intrusion classifiers and results in low robustness to unknown anomalies. In this paper, we propose a novel Imbalanced Generative Adversarial Network (IGAN) to tackle the class imbalance problem. In the primary novelty of our model, we introduce an imbalanced data filter and convolutional layers to the typical GAN, generating new representative instances for minority classes. Further, an IGAN-based Intrusion Detection System, namely IGAN-IDS, is established to cope with class-imbalanced intrusion detection, using the instances generated by IGAN. Concretely, IGAN-IDS consists of three modules: feature extraction, IGAN, and deep neural network. First, we utilize a feed-forward neural network (FNN) to transform raw network attributes into feature vectors. Then, the IGAN generates new samples expressed in the latent space. Finally, the deep neural network, composed of convolutional layers and fully-connected layers, executes the final intrusion detection. We conduct experiments on three benchmark datasets to evaluate the performance of IGAN-IDS, comparing against 15 other methods. The experimental results demonstrate that our proposed IGAN-IDS outperforms the state-of-the-art approaches.