  The boundaries of cyber-physical systems (CPS) and the Internet of Things
(IoT) are converging together day by day to introduce a common platform on
hybrid systems. Moreover, the combination of artificial intelligence (AI) with
CPS creates a new dimension of technological advancement. All these
connectivity and dependability are creating massive space for the attackers to
launch cyber attacks. To defend against these attacks, intrusion detection
system (IDS) has been widely used. However, emerging CPS technologies suffer
from imbalanced and missing sample data, which makes the training of IDS
difficult. In this paper, we propose a generative adversarial network (GAN)
based intrusion detection system (G-IDS), where GAN generates synthetic
samples, and IDS gets trained on them along with the original ones. G-IDS also
fixes the difficulties of imbalanced or missing data problems. We model a
network security dataset for an emerging CPS using NSL KDD-99 dataset and
evaluate our proposed model's performance using different metrics. We find that
our proposed G-IDS model performs much better in attack detection and model
stabilization during the training process than a standalone IDS.
