# Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things

## TL;DR

This paper introduces a distributed GAN-based intrusion detection system for IoT that enhances security and privacy by operating without central control or data sharing, achieving higher accuracy and lower false positives.

## Contribution

The paper proposes a novel distributed GAN architecture for IoT intrusion detection that preserves data privacy and improves detection performance over standalone methods.

## Key findings

- Up to 20% higher intrusion detection accuracy
- 25% higher precision in identifying attacks
- 60% lower false positive rate

## Abstract

To reap the benefits of the Internet of Things (IoT), it is imperative to secure the system against cyber attacks in order to enable mission critical and real-time applications. To this end, intrusion detection systems (IDSs) have been widely used to detect anomalies caused by a cyber attacker in IoT systems. However, due to the large-scale nature of the IoT, an IDS must operate in a distributed manner with minimum dependence on a central controller. Moreover, in many scenarios such as health and financial applications, the datasets are private and IoTDs may not intend to share such data. To this end, in this paper, a distributed generative adversarial network (GAN) is proposed to provide a fully distributed IDS for the IoT so as to detect anomalous behavior without reliance on any centralized controller. In this architecture, every IoTD can monitor its own data as well as neighbor IoTDs to detect internal and external attacks. In addition, the proposed distributed IDS does not require sharing the datasets between the IoTDs, thus, it can be implemented in IoTs that preserve the privacy of user data such as health monitoring systems or financial applications. It is shown analytically that the proposed distributed GAN has higher accuracy of detecting intrusion compared to a standalone IDS that has access to only a single IoTD dataset. Simulation results show that, the proposed distributed GAN-based IDS has up to 20% higher accuracy, 25% higher precision, and 60% lower false positive rate compared to a standalone GAN-based IDS.