Volume 30 Issue 1
Jan.  2021
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Article Contents
ZHANG Lin, HUANG Yanwen, XUAN Jie, FU Xiong, LIN Qiaomin, WANG Ruchuan. Trust Evaluation Model Based on PSO and LSTM for Huge Information Environments[J]. Chinese Journal of Electronics, 2021, 30(1): 92-101. doi: 10.1049/cje.2020.12.005
Citation: ZHANG Lin, HUANG Yanwen, XUAN Jie, FU Xiong, LIN Qiaomin, WANG Ruchuan. Trust Evaluation Model Based on PSO and LSTM for Huge Information Environments[J]. Chinese Journal of Electronics, 2021, 30(1): 92-101. doi: 10.1049/cje.2020.12.005

Trust Evaluation Model Based on PSO and LSTM for Huge Information Environments

doi: 10.1049/cje.2020.12.005
Funds:

the National Natural Science Foundation of China 61572260

the National Natural Science Foundation of China 61872196

the National Natural Science Foundation of China 61872194

the National Natural Science Foundation of China 61402241

Scientific & Technological Support Project of Jiangsu Province BE2017166

the Jiangsu Natural Science Foundation for Excellent Young Scholar BK20160089

Research of Natural Science of NJUPT NY217050

More Information
  • Author Bio:

    HUANG Yanwen   was born in 1995. She is currently pursuing a Master degree in Nanjing University of posts and Telecommunications, China. Her research interests include trust model and data mining. (Email: 15261826052@163.com)

    XUAN Jie   was born in 1993. He received the M.S. degree in information security in Nanjing University of Posts and Telecommunications in 2018. His research interests are focused on network security, trust model and data mining. (Email: 614034012@qq.com)

    FU Xiong   was born in 1979. He received the Ph.D. degree in computer technology from University of Science and Technology of China in 2007. Now, he is an associate professor in Nanjing University of Posts and Telecommunications. His research interests are focused on cloud computing and distributed computing. (Email: fux@njupt.edu.cn)

    LIN Qiaomin   was born in 1979. He received the Ph.D. degree in information network from the Nanjing University of Posts and Telecommunications in 2011. Now, he is an associate professor in the same university. His research interests are focused on privacy protection and Internet of things. (Email: lqm@njupt.edu.cn)

    WANG Ruchuan   was born is 1943. Now he is a professor and a doctorial tutor in Nanjing University of Posts and Telecommunications. He can also enroll new postdoctoral researchers in information and communication engineering in the same college. His research interests are computer software, computer network, information security and Wireless Sensor Network. (Email: wangrc@njupt.edu.cn)

  • Corresponding author: ZHANG Lin   (corresponding author) was born in 1980. She received the Ph.D. degree in information network from the Nanjing University of Posts and Telecommunications in 2009. Now, she is a postdoctoral researcher and is an associate professor in the same university. Her research interests are focused on service computing, network security and trust model. (Email: zhangl@njupt.edu.cn)
  • Received Date: 2018-09-24
  • Accepted Date: 2019-09-10
  • Publish Date: 2021-01-01
  • Due to the challenge of increasing data volume, the traditional trust model is unable to manage data with high efficiency and effectively extract useful information hidden in big data. To fully utilize big data and combine machine learning with trust evaluation, a trust evaluation model based on Long short-term memory (LSTM) is presented. The powerful learning ability, expressive ability and dynamic timing of LSTM can be applied to study data while avoiding the vanishing and exploding gradient phenomena of traditional Recurrent neural networks (RNNs) to ensure that the model can learn sequences of random length and provide accurate trust evaluation. Targeting the performance instability caused by the LSTM model's random initialization of weights and thresholds, Particle swarm optimization (PSO), one of the intelligent algorithms, is introduced to find global optimal initial weights and thresholds. Experiments proved that the trust model proposed in this paper has high accuracy and contributes a new idea for trust evaluation in big data environments.
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