边缘计算中的网络安全：一种信任感知的联邦混合入侵检测框架
Cybersecurity in Edge Computing: A Trust-Aware Federated Hybrid Intrusion Detection Framework
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中文总结 AI 辅助
针对边缘计算中扩大的攻击面，提出一种信任感知联邦混合入侵检测框架（TA-FHIDF），集成自动编码器、1D-CNN和BiLSTM进行本地特征提取，并通过联邦学习与余弦相似度聚合抵御投毒攻击，在多个基准数据集上验证了高准确性和拜占庭容错性。
中文摘要 AI 辅助
边缘计算通过将数据处理迁移到更接近最终用户和物联网（IoT）设备的位置，已成为现代分布式系统中的关键计算范式。虽然这种范式分散了处理过程，最小化了延迟，并减少了回程带宽拥塞，但它也呈指数级地扩大了网络攻击面。部署在不受管理的管理域中的异构、资源受限的边缘设备呈现出高度脆弱的攻击目标。为了解决这些漏洞而不损害全球数据隐私法规，本文提出了一种新颖的信任感知联邦混合入侵检测框架（TA-FHIDF）。所提出的框架将自动编码器、一维卷积神经网络（1D-CNN）和双向长短期记忆（BiLSTM）模型集成到一个统一的、本地化的深度学习引擎中，该引擎能够自主提取空间和时间特征。模型训练通过联邦学习协作进行，确保原始网络遥测数据在本地网关保持隔离。此外，为了防御对抗性模型投毒攻击，我们引入了一种稳健的服务器端信任感知聚合机制，该机制在全局模型集成之前使用余弦相似度度量评估客户端可靠性。在多向量基准数据集（UNSW-NB15、CICIDS2017和Edge-IIoTset）上的实证评估表明，该框架在对抗攻击场景下具有优越的检测准确性、快速收敛性和高拜占庭容错性。
英文摘要
Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Internet of Things (IoT) devices. While this paradigm decentralizes processes, minimizes latency, and reduces backhaul bandwidth congestion, it exponentially enlarges the cyberattack surface. Heterogeneous, resource-constrained edge devices deployed across unmanaged administrative domains present highly vulnerable targets. To address these vulnerabilities without compromising global data privacy regulations, this paper proposes a novel Trust-Aware Federated Hybrid Intrusion Detection Framework (TA-FHIDF). The proposed framework integrates an Autoencoder, a 1D Convolutional Neural Network (1D-CNN), and a Bidirectional Long Short-Term Memory (BiLSTM) model into a unified, localized deep learning engine capable of autonomous spatial and temporal feature extraction. Model training is performed collaboratively via federated learning, ensuring raw network telemetry remains isolated at local gateways. Furthermore, to defend against adversarial model poisoning attacks, we introduce a robust server-side trust-aware aggregation mechanism that evaluates client reliability using a cosine similarity metric before global model integration. Empirical evaluations across multi-vector benchmark datasets (UNSW-NB15, CICIDS2017, and Edge-IIoTset) demonstrate the framework's superior detection accuracy, rapid convergence, and high Byzantine fault tolerance under adversarial attack scenarios.
发表机构
- Victoria University（维多利亚大学）
机构由 AI 辅助整理，请以论文原文为准。
