Dynamic trust evaluation method for IoT nodes

doi:10.11772/j.issn.1001-9081.2019020315

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (9): 2597-2603.DOI: 10.11772/j.issn.1001-9081.2019020315

• Cyber security • Previous Articles Next Articles

Dynamic trust evaluation method for IoT nodes

XIE Lixia, WEI Ruixin

College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China

Received:2019-02-27 Revised:2019-04-28 Online:2019-05-14 Published:2019-09-10
Supported by:
This work is partially supported by the National Science and Technology Major Project (2012ZX03002002), the Civil Aviation Joint Research Fund Project of National Natural Science Foundation of China (U1833107), the Fundamental Research Funds for the Central Universities (ZYGX2018028).

物联网节点动态信任度评估方法

谢丽霞, 魏瑞炘

中国民航大学计算机科学与技术学院, 天津 300300

通讯作者: 谢丽霞
作者简介:谢丽霞(1974-),女,重庆人,教授,博士,CCF会员,主要研究方向:信息安全;魏瑞炘(1994-),女,湖南长沙人,硕士研究生,主要研究方向:信息安全。
基金资助:
国家科技重大专项（2012ZX03002002）；国家自然科学基金民航联合研究基金资助项目（U1833107）；中央高校基本科研业务费资助项目（ZYGX2018028）。

Abstract

Abstract:

In order to solve the problem that the existing Internet of Things (IoT) trust evaluation method ignores the impact of the timeliness of trust and non-intrusion factors on direct trust evaluation, and is lack of reliability evaluation of trust recommendation nodes, which lead to low trust evaluation accuracy and low capability to deal with malicious nodes, an IoT node Dynamic Trust Evaluation Method (IDTEM) was proposed. Firstly, the quality of service persistence factor for nodes was introduced to evaluate node behavior and the dynamic trust attenuation factor of nodes was used to express the timeliness of trust, improving the Bayesian-based direct trust evaluation method. Secondly, the reliability of recommended node was evaluated from three aspects:recommended node value, evaluation difference and trust value of the node itself, and was used to optimize the recommendation trust weight calculation method. At the same time, recommendation trust feedback mechanism was designed to suppress collaborative malicious recommendation nodes by the feedback error between the actual trust of the service provided node after providing service and the recommendation trust. Finally, the adaptive weights of direct and recommendation trust of the node were calculated based on the entropy to obtain the comprehensive trust value of the node. Experimental results show that compared with the Reputation-based Framework for high integrity Sensor Network model (RFSN) based on Bayesian theory and the Behavior-based IoT Trust Evaluation Method (BITEM), IDTEM has certain advantages in dealing with malicious services and malicious recommendation behaviors, and has lower transmission energy consumption.

Key words: Internet of Things (IoT), trust evaluation, Bayesian theory, recommendation trust, node similarity

摘要：

针对现有物联网（IoT）信任度评估方法未考虑信任的时效性、非入侵因素对直接信任度评估的影响以及缺乏对推荐节点可靠度的评估，造成信任评估准确度低且不能有效应对节点恶意行为的不足，提出一种IoT节点动态信任度评估方法（IDTEM）。首先，设计节点服务质量持续因子评估节点行为，并采用动态信任衰减因子表达信任的时效性，改进基于贝叶斯的直接信任度评估方法；其次，从推荐节点价值、评价离散度与节点自身的信任度值三个方面评估推荐节点可靠度，并据此优化推荐信任度权重计算方法；同时，设计推荐信任反馈机制，通过服务提供节点完成服务后的实际信任度与推荐信任度的反馈误差实现对协同恶意推荐节点的惩罚；最后，基于熵计算节点自适应权重，得到节点综合信任度值。实验结果表明，同基于贝叶斯理论的面向无线传感器网络的信誉信任评估框架（RFSN）模型及基于节点行为的物联网信任度评估方法（BITEM）相比，IDTEM可较好地识别恶意服务和抑制恶意推荐行为，且具有较低的传输能耗。

关键词: 物联网, 信任度评估, 贝叶斯理论, 推荐信任, 节点相似度

CLC Number:

TP309

XIE Lixia, WEI Ruixin. Dynamic trust evaluation method for IoT nodes[J]. Journal of Computer Applications, 2019, 39(9): 2597-2603.

谢丽霞, 魏瑞炘. 物联网节点动态信任度评估方法[J]. 计算机应用, 2019, 39(9): 2597-2603.

References

[1] 沈苏彬,林闯.专题前言:物联网研究的机遇与挑战[J].软件学报, 2014,25(8):1621-1624.(SHEN S B, LIN C. Opportunities and challenges in study of Internet of things[J]. Journal of Software, 2014, 25(8):1621-1624.)
[2] YE Z, WEN T, LIU Z, et al. An efficient dynamic trust evaluation model for wireless sensor networks[J]. Journal of Sensors, 2017, 2017:Article ID 7864671.
[3] SUN N, XU J, WEI H, et al. A network state based reliability evaluation model for WSNs[C]//Proceedings of the 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Piscataway, NJ:IEEE, 2017:303-308.
[4] GANERIWAL S, SRIVASTAVA M B. Reputation-based framework for high integrity sensor networks[C]//Proceedings of the 2nd ACM Workshop on Security of Ad Hoc and Sensor Networks. New York:ACM, 2004:66-77.
[5] PRIAYOHESWARI B, KULOTHUNGAN K, KANNAN A. Beta reputation and direct trust model for secure communication in wireless sensor networks[C]//Proceedings of the 2016 International Conference on Informatics and Analytics. New York:ACM, 2016:Article No. 73.
[6] SATHISH S, AYYASAMY A, ARCHANA M. An intelligent beta reputation and dynamic trust model for secure communication in wireless networks[M]//BHATTACHARYYA, SEN S, DUTTA M, et al. Industry Interactive Innovations in Science, Engineering and Technology, LNNS 11. Berlin:Springer, 2017:395-402.
[7] 刘宴兵,龚雪红,冯艳芬.基于物联网节点行为检测的信任评估方法[J].通信学报,2014,35(5):8-15.(LIU Y B, GONG X H, FENG Y F. Trust system based on node behavior detection in Internet of things[J]. Journal on Communications, 2014, 35(5):8-15.)
[8] 弭宝瞳,梁循,张树森.社交物联网研究综述[J].计算机学报,2018,41(7):1441-1475.(MI B T, LIANG X, ZHANG S S. A survey on social Internet of things[J]. Chinese Journal of Computers, 2018,41(7):1441-1475.)
[9] ABDERRAHIM O B, ELHDHILI M H, SAIDANE L. TMCoI-SIOT:a trust management system based on communities of interest for the social Internet of Things[C]//Proceedings of the 13th International Wireless Communications and Mobile Computing Conference. Piscataway, NJ:IEEE, 2017:747-752.
[10] BAO F, CHEN I. Trust management for the Internet of things and its application to service composition[C]//Proceedings of the 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks. Piscataway, NJ:IEEE, 2012:1-6.
[11] CHEN I R, BAO F, GUO J. Trust-based service management for social Internet of things systems[J]. IEEE Transactions on Dependable and Secure Computing, 2016, 13(6):684-696.
[12] MCKNIGHT D H, CHERVANY N L. What trust means in e-commerce customer relationships:an interdisciplinary conceptual typology[J]. International Journal of Electronic Commerce, 2001, 6(2):35-59.
[13] ATZORI L, IERA A, MORABITO G. SIoT:giving a social structure to the Internet of things[J]. IEEE Communications Letters, 2011, 15(11):1193-1195.
[14] 徐军,钟元生,万树平.一种集成直觉模糊信息的激励自适应信任模型[J].电子与信息学报,2016,38(4):803-810.(XU J, ZHONG Y S, WAN S P. Incentive adaptive trust model based on integrated intuitionistic fuzzy information[J]. Journal of Electronics and Information Technology, 2016, 38(4):803-810.)
[15] 游静,上官经伦,徐守坤,等.考虑信任可靠度的分布式动态信任管理模型[J].软件学报,2017,28(9):2354-2369.(YOU J, SHANGGUAN J L, XU S K, et al. Distributed dynamic trust management model based on trust reliability[J]. Journal of Software, 2017, 28(9):2354-2369.)

Dynamic trust evaluation method for IoT nodes

物联网节点动态信任度评估方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	Ziqian CHEN, Kedi NIU, Zhongyuan YAO, Xueming SI. Review of blockchain lightweight technology applied to internet of things [J]. Journal of Computer Applications, 2024, 44(12): 3688-3698.
[2]	Kedi NIU, Min LI, Zhongyuan YAO, Xueming SI. Review of blockchain consensus algorithms for internet of things [J]. Journal of Computer Applications, 2024, 44(12): 3678-3687.
[3]	Yicheng WAN, Guangxiang YANG, Qingda ZHANG, Chenyang GAN, Lin YI. Impact of non-persistent carrier sense multiple access mechanism on scalability of LoRa networks [J]. Journal of Computer Applications, 2023, 43(9): 2885-2896.
[4]	Xingshen WEI, Peng GAO, Zhuo LYU, Yongjian CAO, Jian ZHOU, Zhihao QU. Adaptive interaction feedback based trust evaluation mechanism for power terminals [J]. Journal of Computer Applications, 2023, 43(6): 1878-1883.
[5]	Lin ZHOU, Yuzhi XIAO, Peng LIU, Youpeng QIN. Community mining algorithm based on multi-relationship of nodes and its application [J]. Journal of Computer Applications, 2023, 43(5): 1489-1496.
[6]	Xu WANG, Yumin SHEN, Xiaoyun XIONG, Peng LI, Jinlong WANG. Data management method for building internet of things based on Hashgraph [J]. Journal of Computer Applications, 2022, 42(8): 2471-2480.
[7]	Jie ZHANG, Shanshan XU, Lingyun YUAN. Internet of things access control model based on blockchain and edge computing [J]. Journal of Computer Applications, 2022, 42(7): 2104-2111.
[8]	Ning DONG, Xiaorong CHENG, Mingquan ZHANG. Intrusion detection system with dynamic weight loss function based on internet of things platform [J]. Journal of Computer Applications, 2022, 42(7): 2118-2124.
[9]	Hongqiu LUO, Shengbo HU. Data naming mechanism of low earth orbit satellite mega-constellation for internet of things [J]. Journal of Computer Applications, 2022, 42(7): 2146-2154.
[10]	Xin ZHENG, Suyue LI, Anhong WANG, Meiling LI, Sami MUHAIDAT, Aiping NING. Ergodic rate analysis of cooperative multiple input multiple output ambient backscatter communication system [J]. Journal of Computer Applications, 2022, 42(3): 974-979.
[11]	Yang LI, Anbiao WU, Ye YUAN, Linlin ZHAO, Guoren WANG. Unsupervised attributed graph embedding model based on node similarity [J]. Journal of Computer Applications, 2022, 42(1): 1-8.
[12]	TIAN Zhihong, ZHAO Jindong. Overview of blockchain consensus mechanism for internet of things [J]. Journal of Computer Applications, 2021, 41(4): 917-929.
[13]	BAO Yulong, ZHU Xueyang, ZHANG Wenhui, SUN Pengfei, ZHAO Yingqi. Formal verification of smart contract for access control in IoT applications [J]. Journal of Computer Applications, 2021, 41(4): 930-938.
[14]	ZHANG Lingzhe, HUANG Xiangdong, QIAO Jialin, GOU Wangminhao, WANG Jianmin. Two-stage file compaction framework by log-structured merge-tree for time series data [J]. Journal of Computer Applications, 2021, 41(3): 618-622.
[15]	LI Xiuyan, LIU Mingxi, SHI Wenbo, DONG Guofang. Efficient dynamic data audit scheme for resource-constrained users [J]. Journal of Computer Applications, 2021, 41(2): 422-432.