Industrial Internet of Things Anti-Intrusion Detection System by Neural Network in the Context of Internet of Things for Privacy Law Security Protection

Abstract

Today, the Industrial Internet of Things (IIoT) and network technology are highly developed, and network data breaches occur every year. Therefore, an anti-intrusion detection system has been established to improve the privacy law security protection of IIoT. In the adversarial network, the security performance requirements and structural system of the Internet of Things have high-strength requirements. The network system must adopt a system with strong stability and a low data loss rate. After comparing a large number of network structures, the initial network technology in deep learning is adopted. The Convolutional Neural Network (CNN) technology for handwritten character recognition optimizes and upgrades the LeNet-5 network, and the new LeNet-7 is built. Additionally, three network technologies are combined, and an IIoT anti-intrusion detection system is constructed. The performance of the system is tested and verified. The model has high data accuracy, detection rate, and low false-positive rate. The model’s generality on high-performance data is validated and compared with privacy-aware task offloading methods, achieving the best performance. Therefore, the system can be applied to the data privacy law security protection of IIoT.