HybridGuard: Enhancing Minority-Class Intrusion Detection in Dew-Enabled Edge-of-Things Networks

TL;DR

HybridGuard is a novel framework combining machine learning, deep learning, and generative models to improve intrusion detection in Dew-Enabled Edge-of-Things networks, especially for minority attack classes.

Contribution

It introduces a dual-phase architecture with feature selection and generative data augmentation to enhance detection accuracy for imbalanced intrusion datasets.

Findings

Outperforms existing intrusion detection methods on multiple datasets

Effectively reduces class imbalance through WCGAN-GP

Improves detection of minority attack classes

Abstract

Securing Dew-Enabled Edge-of-Things (EoT) networks against sophisticated intrusions is a critical challenge. This paper presents HybridGuard, a framework that integrates machine learning and deep learning to improve intrusion detection. HybridGuard addresses data imbalance through mutual information based feature selection, ensuring that the most relevant features are used to improve detection performance, especially for minority attack classes. The framework leverages Wasserstein Conditional Generative Adversarial Networks with Gradient Penalty (WCGAN-GP) to further reduce class imbalance and enhance detection precision. It adopts a two-phase architecture called DualNetShield to support advanced traffic analysis and anomaly detection, improving the granular identification of threats in complex EoT environments. HybridGuard is evaluated on the UNSW-NB15, CIC-IDS-2017, and IOTID20 datasets, where it demonstrates strong performance across diverse attack scenarios and outperforms existing solutions in adapting to evolving cybersecurity threats. This approach establishes HybridGuard as an effective tool for protecting EoT networks against modern intrusions.