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

TL;DR

The paper tackles minority-class intrusion detection in Dew-enabled Edge-of-Things networks where data imbalance hampers NIDS performance. It introduces HybridGuard, a framework combining WCGAN-GP based oversampling, mutual information gain feature selection, and a two-phase DualNetShield detector. The approach yields substantial improvements across UNSW-NB15, CIC-IDS2017, and IoTID20, notably reducing false alarms while preserving high detection accuracy. These results demonstrate the practicality of robust, edge-oriented IDS capable of adapting to evolving cybersecurity threats and complex IoT environments.

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.

Figures (9)

• Figure 1: Dew-enabled Edge-of-Things networks.
• Figure 2: Proposed HybridGuard framework.
• Figure 3: Network Structure of WCGAN-GP.
• Figure 4: WCGAN-GP loss curves demonstrate the training performance.
• Figure 5: Workflow diagram of DualNetSheild algorithm.