Username   Password       Forgot your password?  Forgot your username? 


Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006


Trust Authorization Monitoring Model in IoT

Volume 14, Number 3, March 2018, pp. 453-462
DOI: 10.23940/ijpe.18.03.p6.453462

Ruizhong Dua,b, Chong Liua,b, and Fanming Liua,b

aCyberspace Security and Computer College, Baoding, 071002, China
bKey Laboratory on High Trusted Information System in Hebei Province, Baoding, 071002, China

(Submitted on December 23, 2017; Revised on January 27, 2018; Accepted on February 24, 2018)


With strong heterogeneity and the limited computing ability of IoT nodes, this dissertation proposes a Trust Authorization Model based on detection feedback in IoT that is combined with the current trust model of IoT as well as implement storage and other tasks. By calculating and storing the cluster head node along with its strong ability to facilitate the data transmission and search for energy consumption, it prevents the local network from being limited by the computing power of the device. In terms of trust calculation, the threshold value is based on the recommendation. At the same time, the BP neural network algorithm with self-learning function is periodically detecting the interactive data stream, detecting the attack nodes, quickly implementing the response measures, and meeting the actual situation of unmanned IoT of mass devices. Simulation results show that this model has lower energy consumption than other similar models, has good coping ability for attacks such as malicious recommendation and malicious slander, and has a higher detection rate and response rate to attack nodes.


References: 15

  1. F. Bao, R. Chen, M. J. Chang, et al. “Trust-based Intrusion Detection in Wireless Sensor Networks” [A]. 2011 IEEE International Conference on Communications (ICC)[C]. Kyoto, Japan, 2011.1-6.
  2. K. Bloede, G. Mischou, A. Senan, and R. Koontz, “The Internet of Things,” Available at IOT- M and A- REPORT- 2015-3.pdf, Last accessed:2016-10-27.
  3. G. V. Crosby, L. Hester, and N. Pissinou, “Location-aware, Trust-based detection and Isolation of Compromised Nodes in Wireless Sensor Networks” [J]. International Journal Network Security, 2011, 12(2): 107- 117.
  4. I. R. Chen, F. Bao, and J. Guo, “Trust-based Service Management for Social Internet of Things Systems,” IEEE Trans. Dependable Secur. Dependable Secur. Comput., vol. 5971, no. c, pp. 1–1, 2015.
  5. X. H. Gong. “Research on Secure Clustering Mechanism of IoT-aware Nodes Based on Trust” [D]. Chongqing: Chongqing University of Posts and Telecommunications, 2014.
  6. A. Jøsang, “A Logic for Uncertain Probabilities,” International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, vol. 9, pp.279–311, 2001.
  7. B. B. Liu, X. H. Gong. “Trust Assessment Method Based on IoT Node Behavior Detection” [J]. Journal of Communications, 2014,35 (5): 8-15.
  8. W. M. Liu, L. H. Yin, B. X. Fang and so on.” Study on the Trust Mechanism Under the Internet of Things” [J]. Chinese Journal of Computers, 2012,35 (5): 847-855.
  9. Y. B. Liu, W. P. Hu “Internet of Things Security Model and Key Technologies” [J]. Digital Communications, 010, 37 (4): 28-33,2010.
  10. M. Nitti, R. Girau, and L. Atzori, “Trustworthiness Management in the Social Internet of Things,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 5, pp. 1253–1266, 2014.
  11. ROMANA. R, ZHOUA. J, LOPEZB. J. “On the Features and Challenges of Security & Privacy in Distributed Internet of Things” [J]. Computer Networks, 2013, 57 (10):2266-2279.
  12. Srinivas Mukkamala, Andrew H. Sung. “Identifying Significant Features for Network Forensic Analysis Using Artificial Intelligent Techniques” [J], International Journal of Digital Evidence, Winter 2003,4:63-69.
  13. H. Xu. “Research on the Trust Model of Internet of Things Based on Clustering” [D]. Lanzhou traffic University, 2017.
  14. J. You, Shangguan Lun, Xu Shoukun, et al. “A Distributed Dynamic Trust Management Model Considering Trust Reliability” [J]. Journal of Software, 2017.
  15. A. K. Zeeshan, H. Peter, “A Trust Based Distributed Intrusion Detection Mechanism for Internet of Things”, In 2017 IEEE 31st International Conference on Advanced Information Networking and Applications. IEEE, 2017, pp.1169-1176


Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

Download this file (IJPE-2018-03-06.pdf)IJPE-2018-03-06.pdf[Trust Authorization Monitoring Model in IoT]280 Kb


Prev Next

Temporal Multiscale Consumption Strategies of Intermittent Energy based on Parallel Computing

Huifen Chen, Yiming Zhang, Feng Yao, Zhice Yang, Fang Liu, Yi Liu, Zhiheng Li, and Jinggang Wang

Read more

Decision Tree Incremental Learning Algorithm Oriented Intelligence Data

Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun

Read more

Spark-based Ensemble Learning for Imbalanced Data Classification

Jiaman Ding, Sichen Wang, Lianyin Jia, Jinguo You, and Ying Jiang

Read more

Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang, and Denis Špelič

Read more

An Improved Algorithm based on Time Domain Network Evolution

Guanghui Yan, Qingqing Ma, Yafei Wang, Yu Wu, and Dan Jin

Read more

Auto-Tuning for Solving Multi-Conditional MAD Model

Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie

Read more

Smart Mine Construction based on Knowledge Engineering and Internet of Things

Xiaosan Ge, Shuai Su, Haiyang Yu, Gang Chen, and Xiaoping Lu

Read more

A Mining Model of Network Log Data based on Hadoop

Yun Wu, Xin Ma, Guangqian Kong, Bin Wang, and Xinwei Niu

Read more
This site uses encryption for transmitting your passwords.