G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System

TL;DR

This paper introduces G-IDS, a GAN-based intrusion detection system that enhances cyber-physical system security by generating synthetic data to address imbalanced and missing samples, improving attack detection performance.

Contribution

The paper presents a novel GAN-assisted IDS framework that effectively handles data imbalance and missing samples in CPS security scenarios.

Findings

G-IDS outperforms standalone IDS in attack detection accuracy.

G-IDS stabilizes training and improves detection in imbalanced data conditions.

Synthetic data generation enhances model robustness.

Abstract

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.