混合检测框架提升边缘物联网络少数类攻击识别能力
HybridGuard: Enhancing Minority-Class Intrusion Detection in Dew-Enabled Edge-of-Things Networks
- 融合机器学习与深度学习，用互信息选关键特征
- 在三个数据集上准确率超现有方法，尤其改善少数类检测
- 适合需要高精度异常检测的物联网边缘安全场景
针对智能边缘物联（EoT）网络中的复杂入侵威胁，本文提出HybridGuard框架，结合机器学习与深度学习提升入侵检测能力。通过基于互信息的特征选择缓解数据不平衡问题，聚焦关键特征以增强对少数类攻击的识别性能。采用带梯度惩罚的Wasserstein条件生成对抗网络（WCGAN-GP）进一步降低类别偏差，提高检测精度。框架采用双阶段架构DualNetShield，支持精细化流量分析与异常检测，在复杂EoT环境中实现更精准的威胁定位。在UNSW-NB15、CIC-IDS-2017和IOTID20数据集上验证，HybridGuard在多种攻击场景下表现优异，优于现有方案，具备应对动态网络安全威胁的能力，为EoT网络提供有效防护。
原文摘要 · Abstract (English)
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.
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