IIoT-IDS Network using Inception CNN Model

A. Arun kumar, Radha Krishna Karne
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引用次数: 3

Abstract

Modern network and Industrial Internet of Things (IIoT) technologies are quite advanced. Networks experience data breaches annually. As a result, an Intrusion Detection System is designed for enhancing the IIoT security protection under privacy laws. The Internet of Things' structural system and security performance criteria must meet high standards in an adversarial network. The network system must use a system that is very stable and has a low rate of data loss. The basic deep learning network technology is picked after analysing it with a huge number of other network configurations. Further, the network is upgraded and optimised by the Convolutional Neural Network technique. Additionally, an IIoT anti-intrusion detection system is built by combining three network technologies. The system's performance is evaluated and confirmed. The proposed model gives a better detection rate with a minimum false positive rate, and good data correctness. As a result, the proposed method can be used for securing an IIoT data privacy under the law.
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基于Inception CNN模型的IIoT-IDS网络
现代网络和工业物联网(IIoT)技术相当先进。网络每年都会遭遇数据泄露。因此,入侵检测系统旨在加强隐私法下的工业物联网安全保护。物联网的结构体系和安全性能标准在对抗网络中必须达到高标准。网络系统必须使用非常稳定、数据丢失率低的系统。基本的深度学习网络技术是在与大量其他网络配置进行分析后选择的。此外,利用卷积神经网络技术对网络进行了升级和优化。结合三种网络技术,构建了工业物联网防入侵检测系统。对系统的性能进行了评估和确认。该模型具有较高的检测率和最小的误报率,并且具有良好的数据正确性。因此,根据法律规定,该方法可用于保护工业物联网数据隐私。
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