TY - JOUR
T1 - Mobile Collaborative Secrecy Performance Prediction for Artificial IoT Networks
AU - Xu, Lingwei
AU - Zhou, Xinpeng
AU - Li, Xingwang
AU - Jhaveri, Rutvij H.
AU - Gadekallu, Thippa Reddy
AU - Ding, Yuan
N1 - Funding Information:
This work was supported in part by the Shandong Province Natural Science Foundation under Grant ZR2020QF003, in part by the Open Research Fund of Anhui Engineering Technology Research Center of Automotive New Technique under Grant QCKJ202101, in part by the Opening Foundation of Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education, under Grant K93-9-2021-09, in part by the Doctoral Found of QUST under Grant 1203043003480, in part by the Scientific Research Project of Education Department of Guangdong under Grant 2021KCXTD061, in part by the Science and Technology Program of Guangzhou under Grant 202207010389, and in part by the Key Project of Guizhou Science and Technology Support Program under Grant Guizhou Key Science and Support [2021]-001.
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2022/8
Y1 - 2022/8
N2 - The integration of artificial intelligence 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, the evaluation and prediction of secrecy performance is a very critical research. In this article, new expressions for the nonzero 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 article, 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 2 × 1 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 SI-CNN prediction algorithm is shown to provide better NSCP performance prediction than other state-of-the-art methods. In particular, compared with wavelet neural network, the prediction precision of SI-CNN is improved by 26.8%.
AB - The integration of artificial intelligence 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, the evaluation and prediction of secrecy performance is a very critical research. In this article, new expressions for the nonzero 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 article, 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 2 × 1 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 SI-CNN prediction algorithm is shown to provide better NSCP performance prediction than other state-of-the-art methods. In particular, compared with wavelet neural network, the prediction precision of SI-CNN is improved by 26.8%.
KW - Artificial Internet of Things (AIoT) network
KW - Collaborative secrecy performance
KW - Improved convolutional neural network (CNN)
KW - Intelligent prediction
UR - http://www.scopus.com/inward/record.url?scp=85131174444&partnerID=8YFLogxK
U2 - 10.1109/TII.2021.3128506
DO - 10.1109/TII.2021.3128506
M3 - Article
SN - 1551-3203
VL - 18
SP - 5403
EP - 5411
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 8
ER -