Intelligent Security Performance Prediction for IoT-Enabled Healthcare Networks Using an Improved CNN

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 3; pp. 2063 - 2074
• Main Authors: Xu, Lingwei, Zhou, Xinpeng, Tao, Ye, Liu, Lei, Yu, Xu, Kumar, Neeraj
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Airline security; Algorithms; Artificial intelligence; Artificial neural networks; Big Data; Convolution; Data transmission; Fading channels; Feature extraction; Health care; Improved convolutional neural network (CNN); Internet of Things; Internet of Things (IoT) healthcare networks; Kernel; Medical services; Performance prediction; physical layer security; Prediction algorithms; Real time; Security
• DOI: 10.1109/TII.2021.3082907
• ISSN: 1551-3203

The global healthcare industry and artificial intelligence have promoted the development of the diversified intelligent healthcare applications. Internet of Things (IoT) will play an important role in meeting the high throughput requirements of diversified intelligent healthcare applications. However, the mobile IoT-enabled healthcare networks are diverse and open, the healthcare big data transmission is vulnerable to a potential attack, which can cause network outages and serious healthcare security issues. To process the complex healthcare security event in real time, security performance prediction is critical for mobile IoT-enabled healthcare networks. In this article, we first analyze the security performance, and derive the novel expressions for the security performance in a closed form. Then, to analyze the security performance in real time, a security performance intelligent prediction algorithm is proposed. An improved convolutional neural network (CNN) model is designed, which combines the four-layer convolution and a four-branch inception block, and can adopt different convolution kernels in the same layer. The four-branch inception block can increase the width of the CNN while reducing the parameters. The improved CNN model can not only increases the width of the CNN, extract different sizes of healthcare data features, but also increases the adaptability to the nonlinear healthcare big data. Compared with different methods, the proposed intelligent algorithm can obtain better security performance prediction. In particular, for prediction precision, the proposed intelligent algorithm is increased by 20%.