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

2025-02-09

AI Summary

To address the challenges of detecting rare attacks and severe class imbalance (long-tailed distribution) in 6G-enabled IoT networks, this paper proposes a two-stage data augmentation framework cascading CTGAN and SMOTEENN: first, Conditional Tabular Generative Adversarial Networks (CTGAN) synthesize high-fidelity minority-class attack samples; second, SMOTEENN jointly optimizes oversampling and undersampling to significantly mitigate data skew. This work constitutes the first systematic integration of generative modeling and hybrid sampling techniques for IoT intrusion detection. Experiments on the CSE-CIC-IDS2018 dataset demonstrate an overall accuracy of 99.90% and a rare-attack detection accuracy of 80%, substantially outperforming state-of-the-art baselines. The proposed framework markedly enhances the capability of intrusion detection systems (IDS) to identify infrequent yet high-risk attacks.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Detects rare cyber-attacks in IoT networks
• Addresses class imbalance in IoT data
• Enhances intrusion detection system accuracy

Innovation

• CTGAN for synthetic data generation
• SMOTEENN for dataset enhancement
• DNN-based multiclass intrusion detection