PDF(Pubmed)
Abstract：
The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model\'s detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.
摘要：
工业物联网（IIoT）的安全性至关重要，网络入侵检测系统(NIDS)在其中发挥着不可或缺的作用。尽管关于利用深度学习技术实现网络入侵检测的研究越来越多,由于深度学习需要大规模数据集进行训练，因此设备的本地数据有限可能会导致模型性能不佳。一些解决方案建议集中设备的本地数据集用于深度学习训练，但这可能涉及用户隐私问题。为了应对这些挑战，这项研究提出了一种新颖的基于联邦学习（FL）的方法，旨在提高网络入侵检测的准确性，同时确保数据隐私保护。这项研究将卷积神经网络与注意力机制相结合，开发了一种专门为IIoT设计的新的深度学习入侵检测模型。此外,变分自动编码器被纳入以增强数据隐私保护。此外，FL框架使多个IIoT客户端能够在不共享其原始数据的情况下联合训练共享入侵检测模型。此策略显著提高了模型的检测能力，同时有效解决了数据隐私和安全问题。为了验证该方法的有效性,在真实世界的物联网(IoT)网络入侵数据集上进行了一系列实验。实验结果表明，我们的模型和FL方法显著提高了关键性能指标，如检测精度，精度,与传统的局部训练方法和现有模型相比，以及假阳性率(FPR)。
