发表机构
University of Stuttgart（斯图加特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对物联网异构安全模型易受攻击的问题，提出GCT自动编码器跨模态深度学习框架，集成边缘智能与社区检测注意力，在入侵检测数据集上准确率达0.908，损失降至0.00156。
AI 中文摘要
物联网涵盖了从智能家居设备到自动驾驶汽车等多样化的物理实体，形成了一个具有异构安全模型的复杂环境。这种异构性使得物联网子系统容易受到各种网络攻击。因此，现代安全系统必须更加健壮，以确保物联网应用的安全性和隐私性。一个高度安全的物联网系统还需要实时洞察，这要求在计算层的边缘进行数据收集。这种多样性要求在基础层面应用统一的安全模型。边缘智能提供了一种直接处理设备多样性的方法。物联网中边缘智能的一个关键目标是从本地数据中提取洞察；安全模型随后可以利用这些数据构建本地节点保护，而集成AI模型则能产生先进的安全解决方案。本研究提出了一种新颖的深度学习算法，用于在边缘进行有效的入侵检测，并得到了基于云的物联网框架的支持。我们将所提出的跨模态深度学习算法与基线模型进行了评估。其贡献是一种用于入侵检测的跨领域深度神经网络（DNN）算法。目标是通过带有建模注意力的社区检测，评估一种多方法深度学习模型，以在边缘检测物联网系统中的入侵。我们评估了GCT自动编码器，这是一种集成边缘智能以识别安全缺陷的新颖框架。该模型显著提高了性能和效率。在一个涵盖多种攻击场景的网络入侵物联网数据集上，它达到了0.908的准确率，将学习损失降低至0.00156，并优于现有方法。
英文摘要
IoT encompasses diverse physical entities, from smart home devices to autonomous vehicles, creating a complex environment with heterogeneous security models. This heterogeneity makes IoT sub-systems vulnerable to various network attacks. Modern security systems must therefore be more robust to ensure security and privacy for IoT applications. A highly secure IoT system also demands real time insight, requiring data collection at the edge of the computing layer. This diversity calls for a unified security model applied at the foundational level. Edge intelligence offers a direct approach to handling device diversity. A key goal of edge intelligence in IoT is to extract insight from local data; security models can then use this data to build local node protections, and integrating AI models yields an advanced security solution. This research proposes a novel deep learning algorithm for effective intrusion detection at the edge, supported by a cloud-based IoT framework. We evaluate the proposed cross modal deep learning algorithm against baseline models. The contribution is a cross domain Deep Neural Network (DNN) algorithm for intrusion detection. The objective is to assess a multi-method deep learning model to detect intrusions in IoT systems at the edge via community detection with modeled attention. We evaluate GCT auto-encoder, a novel framework integrating edge intelligence to identify security flaws. The model significantly improves performance and efficiency. On a network intrusion IoT dataset covering multiple attack scenarios, it achieved 0.908 accuracy, reduced learning loss to 0.00156, and outperformed existing approaches.
