In the realm of IoT/CPS systems connected over mobile networks, traditional intrusion detection methods analyze network traffic across multiple devices using anomaly detection techniques to flag potential security threats. However, these methods face significant privacy challenges, particularly with deep packet inspection and network communication analysis. This type of monitoring is highly intrusive, as it involves examining the content of data packets, which can include personal and sensitive information. Such data scrutiny is often governed by stringent laws and regulations, especially in environments like smart homes where data privacy is paramount. Synthetic data offers a promising solution by mimicking real network behavior without revealing sensitive details. Generative models such as Generative Adversarial Networks (GANs) can produce synthetic data, but they often struggle to generate realistic data in specialized domains like network activity. This limitation stems from insufficient training data, which impedes the model's ability to grasp the domain's rules and constraints adequately. Moreover, the scarcity of training data exacerbates the problem of class imbalance in intrusion detection methods. To address these challenges, we propose a Privacy-Driven framework that utilizes a knowledge-infused Generative Adversarial Network for generating synthetic network activity data (KiNETGAN). This approach enhances the resilience of distributed intrusion detection while addressing privacy concerns. Our Knowledge Guided GAN produces realistic representations of network activity, validated through rigorous experimentation. We demonstrate that KiNETGAN maintains minimal accuracy loss in downstream tasks, effectively balancing data privacy and utility.

翻译：在通过移动网络连接的物联网/信息物理系统领域，传统的入侵检测方法采用异常检测技术分析多设备间的网络流量，以标记潜在的安全威胁。然而，这些方法面临重大的隐私挑战，特别是在深度数据包检测和网络通信分析方面。此类监控具有高度侵入性，因为它涉及检查数据包的内容，其中可能包含个人敏感信息。这种数据审查通常受到严格法律法规的约束，在智能家居等数据隐私至关重要的环境中尤为突出。合成数据通过模拟真实网络行为而不暴露敏感细节，提供了一种前景广阔的解决方案。生成对抗网络等生成模型能够产生合成数据，但在网络活动等专业领域中往往难以生成逼真的数据。这一局限性源于训练数据不足，阻碍了模型充分掌握领域规则与约束的能力。此外，训练数据的稀缺加剧了入侵检测方法中类别不平衡的问题。为应对这些挑战，我们提出了一种隐私驱动的框架，利用知识增强的生成对抗网络来生成合成网络活动数据（KiNETGAN）。该方法在解决隐私问题的同时，增强了分布式入侵检测的鲁棒性。我们的知识引导生成对抗网络能生成逼真的网络活动表征，并通过严格实验验证。我们证明KiNETGAN在下游任务中保持极低的精度损失，有效平衡了数据隐私与实用性。