A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks

TL;DR

This paper introduces a two-stage intrusion detection system for IoT networks that uses conditional GANs to generate synthetic data for rare attacks, significantly improving detection accuracy.

Contribution

It presents a novel combination of conditional GANs and SMOTEENN for data augmentation to enhance rare attack detection in IoT security systems.

Findings

Achieved 99.90% overall accuracy in IDS

Detected rare attacks with 80% accuracy

Demonstrated effectiveness of synthetic data augmentation

Abstract

Internet of things (IoT) networks, boosted by 6G technology, are transforming various industries. However, their widespread adoption introduces significant security risks, particularly in detecting rare but potentially damaging cyber-attacks. This makes the development of robust IDS crucial for monitoring network traffic and ensuring their safety. Traditional IDS often struggle with detecting rare attacks due to severe class imbalances in IoT data. In this paper, we propose a novel two-stage system called conditional tabular generative synthetic minority data generation with deep neural network (CTGSM-DNN). In the first stage, a conditional tabular generative adversarial network (CTGAN) is employed to generate synthetic data for rare attack classes. In the second stage, the SMOTEENN method is applied to improve dataset quality. The full study was conducted using the CSE-CIC-IDS2018 dataset, and we assessed the performance of the proposed IDS using different evaluation metrics. The experimental results demonstrated the effectiveness of the proposed multiclass classifier, achieving an overall accuracy of 99.90% and 80% accuracy in detecting rare attacks.