Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

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
No.6 June 2018
No.6 June 2018
No.7 July 2018
No.7 July 2018
No.8 August 2018
No.8 August 2018
No.9 September 2018
No.9 September 2018
No.10 October 2018
No.10 October 2018
No.11 November 2018
No.11 November 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)


Abstract:

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 http://www.woodsidecap.com/wp-content/uploads/2015/03/WCP- 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

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

CURRENT ISSUE

Prev Next

A Label Propagation Algorithm based on Circular Spread

Yong Wang, Xinzhen Fang, Jiahao Shi, and Jing Yang

Read more

Abnormal Information Identification and Elimination in Cognitive Networks

Ruowu Wu, Xiang Chen, Hui Han, Haojun Zhao, and Yun Lin

Read more

A Framework of Intrusion Detection System based on Bayesian Network in IoT

Qingping Shi, Jian Kang, Rong Wang, Hang Yi, Yun Lin, and Jie Wang

Read more

Marine Three-Shaft Intercooled-Cycle Gas Turbine Engine Transient Thermodynamic Simulation

Jingchao Li, Guoyin Zhang, Yulong Ying,Wanying Shi, and Dongyuan Bi

Read more

An Optimization Method for XML Twig Query

Zhixue He, Huan Wang, and Husheng Liao

Read more

An Indoor Fusion Localization Method using Pedestrian Dead Reckoning

Qian Zhao, Peng Luan, Huiqiang Wang, Hongwu Lv, Guangsheng Feng, and Mao Tang

Read more

Delay Constraint Data Collection Strategy in VANET

Huanhuan Yang, Zongpu Jia, and Guojun Xie

Read more

Parallel Optimization of KNN Query Strategy based on Road Network

Boqi Hu, Hailong Sun, Fangsong Li, Chao Jiang, and Weitao Zou

Read more

An Improved TOA Model based on Error Correction and Self-Genetic Algorithm

Xuyang Wang, Yaxi Wang, Zhongkai Dang, Hongmei Pei, and Long Zhang

Read more

A Bipartite Graph Matching Algorithm in Human-Computer Collaboration

Junfeng Man, Longqian Zhao, Ming Liu, Cheng Peng, and Qianqian Li

Read more

A Distributed Secure Monitoring System based on Blockchain

Guangsong Yang, Xinwen Wu, Yiliang Wu, and Chincheng Chen

Read more

Design of Outcome-based Education Blockchain

Tao Li, Bin Duan, Dayu Liu, and Zhen Fu

Read more

An Automatic Web Data Extraction Approach based on Path Index Trees

Yan Wen, Qingtian Zeng, Hua Duan, Feng Zhang, and Xin Chen

Read more

Deep Web Entity Identification Method with Unique Constraint

Xuefeng Xian, Pengpeng Zhao, Zhaobin Liu, Caidong Gu, and Victor S. Sheng

Read more

A Hierarchical Caching Decision Algorithm for Content-Centric Network

Zengyu Cai, Xuhui Wang, Jianwei Zhang, Wanwei Huang, and Yong Gan

Read more

New Polling Scheme based on Busy/Idle Queues Mechanism

Zhijun Yang, Yangyang Sun, and Jianhou Gan

Read more
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com