P2P网络中基于权重因素的信任模型

doi:10.3724/SP.J.1087.2013.01612

计算机应用 ›› 2013, Vol. 33 ›› Issue (06): 1612-1614.DOI: 10.3724/SP.J.1087.2013.01612

P2P网络中基于权重因素的信任模型

陈珊珊

南京邮电大学海外教育学院，南京 210003

收稿日期:2013-01-04 修回日期:2013-03-06 出版日期:2013-06-01 发布日期:2013-06-05
通讯作者: 陈珊珊
作者简介:陈珊珊（1980-），女，安徽安庆人，副教授，博士，主要研究方向:计算机网络、信息安全。
基金资助:
国家973计划项目（2011CB302903）;国家自然科学基金资助项目（61272084）

Trust model based on weight factor in P2P network

CHEN Shanshan

College of Oversea Education, Nanjing University of Posts and Telecommunications, Nanjing Jiangsu 210003, China

Received:2013-01-04 Revised:2013-03-06 Online:2013-06-05 Published:2013-06-01
Contact: CHEN Shanshan
Supported by:
Theory and practice of the Internet of Things

摘要/Abstract

摘要： 针对P2P网络内部的安全问题，提出了一种P2P网络基于直接交易信任和推荐信任的模型，运用了直接交易信息参数、推荐信息的评价可信度和动态平衡权值参数，较简单准确地描述了节点的综合信任值，在进行交易前与目标节点建立信任关系，能有效抑制恶意节点对网络中其他节点的恶意交易行为和评价欺骗，提高网络交易的安全性。

关键词: 直接信任, 推荐信任, 动态平衡权重, 恶意

Abstract: According to the inner security issue of Peer-to-Peer (P2P) networks, a trust model based on direct transaction and recommendation in P2P network was proposed. Parameters of direct transaction information, rating confidence degree of recommendation information and dynamic balance weight were applied in the model. The model described peer’s integration trust simply and accurately, and established trust relationship with target peer before transaction, which can restrain malicious peer to do malice behavior and rating cheat to other peers in the network, and improve the security of network trade.

Key words: direct trust, recommendation trust, dynamic balance weight, malice

中图分类号:

TP309

陈珊珊. P2P网络中基于权重因素的信任模型[J]. 计算机应用, 2013, 33(06): 1612-1614.

CHEN Shanshan. Trust model based on weight factor in P2P network[J]. Journal of Computer Applications, 2013, 33(06): 1612-1614.

参考文献

[1] GKANTSIDIS C, MIHAIL M, SABERI A. Random walks in peer-to-peer networks: Algorithms and evaluation［J］. Performance Evaluation, 2006, 63(3):241-263.

[2] MASSOULIL, le MERRER E, KERMARREC A M, et al. Peer counting and sampling in overlay networks: random walk methods［C］// Proceedings of the 25th Annual ACM Symposium on Principles of Distributed Computing. New York:ACM, 2006 :123-132.

[3] SABATER J, SIERRA C. Reputation and social network analysis in multi-Agent systems［C］// Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems. New York :ACM, 2002:1-8.

[4] KAMVAR S, SCHLOSSER M, GARCIA-MOLINA H. The eigentrust algorithm for reputation management in P2P networks［C］// Proceedings of the 12th International Conference on World Wide Web. New York :ACM, 2003:640-651.

[5] XIONG L, LIU L. PeerTrust: Supporting reputation-based trust for peer-to-peer electronic communities［J］. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(7):843-857.

[6] ZHOU R, HWANG K. PowerTrust: A robust and scalable reputation system for trusted peer-to-peer computing［J］. IEEE Transactions on Parallel and Distributed Systems, 2007, 18(4):460-473.

[7] ZHOU R, HWANG K, CAI M. GossipTrust for fast reputation aggregation in peer-to-peer networks［J］. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(9):1282-1295.

[8] LIANG Z Q, SHI W S. PET: A personal trust model with reputation and risk evaluation for P2P resource sharing［C］// Proceedings of the 38th Annual Hawaii International Conference on System Sciences. Washington, DC：IEEE Computer Society, 2005:201.

[9] 常俊胜, 王怀民, 尹刚. DyTrust:一种P2P系统中基于时间帧的动态信任模型［J］. 计算机学报, 2006, 29(8):1301-1307.

[10] AZZEDIN F, RIDHA A. Feedback behavior and its role in trust assessment for peer-to-peer systems［J］. Telecommunication Systems, 2010(44):253-266.

[11] CHU X, CHEN X, ZHAO K, et al. Reputation and trust management in heterogeneous peer-to-peer networks［J］. Telecommunication Systems, 2010,44(3/4):191-203.

[12] MARMOL F, PEREZ G. Security threats scenarios in trust and reputation models for distributed systems［J］. Computers & Security, 2009, 28(7):545-556.

[13] ANDROUTSELLIS-THEOTOKIS S, SPINELLIS D, LOURIDAS P, et al. A market-based approach to managing the risk of peer-to-peer transactions［J］. Computer Networks, 2010, 54(5):675-688.

[14] WANG Y, NAKAO A. Poisonedwater: An improved approach for accurate reputation ranking in P2P networks［J］. Future Generation Computer Systems, 2010, 26(8):1317-1326.

[1]	蒋考林, 白玮, 张磊, 陈军, 潘志松, 郭世泽. 基于多通道图像深度学习的恶意代码检测[J]. 计算机应用, 2021, 41(4): 1142-1147.
[2]	姜倩玉, 王凤英, 贾立鹏. 基于感知哈希算法和特征融合的恶意代码检测方法[J]. 计算机应用, 2021, 41(3): 780-785.
[3]	陈旖, 张美璟, 许发见. 基于一维卷积神经网络的HTTP慢速DoS攻击检测方法[J]. 计算机应用, 2020, 40(10): 2973-2979.
[4]	王博, 蔡弘昊, 苏旸. 基于VGGNet的恶意代码变种分类[J]. 计算机应用, 2020, 40(1): 162-167.
[5]	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.
[6]	郎大鹏, 丁巍, 姜昊辰, 陈志远. 基于多特征融合的恶意代码分类算法[J]. 计算机应用, 2019, 39(8): 2333-2338.
[7]	杨萍, 赵冰, 舒辉. 基于图标相似性分析的恶意代码检测方法[J]. 计算机应用, 2019, 39(6): 1728-1734.
[8]	朴杨鹤然, 任俊玲. 基于Stacking的恶意网页集成检测方法[J]. 计算机应用, 2019, 39(4): 1081-1088.
[9]	赵宏, 常兆斌, 王乐. 基于词法特征的恶意域名快速检测算法[J]. 计算机应用, 2019, 39(1): 227-231.
[10]	罗世奇, 田生伟, 禹龙, 于炯, 孙华. 基于纹理指纹与活动向量空间的Android恶意代码检测[J]. 计算机应用, 2018, 38(4): 1058-1063.
[11]	谢丽霞, 李爽. 基于Bagging-SVM的Android恶意软件检测模型[J]. 计算机应用, 2018, 38(3): 818-823.
[12]	蒋晨, 胡玉鹏, 司凯, 旷文鑫. 基于图像纹理和卷积神经网络的恶意文件检测方法[J]. 计算机应用, 2018, 38(10): 2929-2933.
[13]	吴若豪, 董平, 郑涛. 基于OpenDayLight的恶意扫描防护技术[J]. 计算机应用, 2018, 38(1): 188-193.
[14]	罗文塽, 曹天杰. 基于非用户操作序列的恶意软件检测方法[J]. 计算机应用, 2018, 38(1): 56-60.
[15]	苏志达, 祝跃飞, 刘龙. 基于深度学习的安卓恶意应用检测[J]. 计算机应用, 2017, 37(6): 1650-1656.

P2P网络中基于权重因素的信任模型

Trust model based on weight factor in P2P network

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics