Mobile Collaborative Secrecy Performance Prediction for Artificial IoT Networks

Lingwei Xu, Xinpeng Zhou, Xingwang Li, Rutvij H. Jhaveri, Thippa Reddy Gadekallu, Yuan Ding

Research output: Contribution to journalArticlepeer-review

13 Downloads (Pure)

Abstract

The integration of artificial intelligence (AI) and Internet of Things (IoT) has promoted the rapid development of artificial IoT (AIoT) networks. A wide range of AIoT applications have generated a great deal of data. The fifth-generation (5G) mobile communication has powerful data processing capabilities, and it is a key technology to enable AIoT big data processing.
The explosive growth of the 5G users has made information security in AIoT networks a significant issue. Real-time security evaluation in AIoT networks is difficult due to user mobility and dynamic wireless environments. Thus, evaluation and prediction of secrecy performance is a very critical research. In this paper, new expressions for the non-zero secrecy capacity probability
(NSCP) are derived to evaluate the mobile collaborative secrecy performance. An improved convolutional neural network (CNN) model, named as SI-CNN in this paper, is proposed to predict the NSCP performance. The SI-CNN model combines the SqueezeNet and InceptionNet, and it has four convolution layers, which all adopt the Same convolution model. For the first two layers,
they employ a 21 convolution and a three-branch convolution, which not only increase the number of channels but also extract more features. For the last two layers, they employ the same structure, but different convolution kernels. The proposed SICNN prediction algorithm is shown to provide better NSCP performance prediction than other state-of-the-art methods. In particular, compared with wavelet neural network (WNN), the prediction precision of SI-CNN is improved by 26.8%.
Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
Publication statusAccepted/In press - 4 Nov 2021

Fingerprint

Dive into the research topics of 'Mobile Collaborative Secrecy Performance Prediction for Artificial IoT Networks'. Together they form a unique fingerprint.

Cite this