一种对抗社交网络链接作弊的算法#br#

doi:10.3969/j.issn.1006-2475.2015.07.001

计算机与现代化

• 算法设计与分析 • 下一篇

一种对抗社交网络链接作弊的算法#br#

1.鞍山师范学院数学与信息科学学院，辽宁鞍山114005； 2.大连理工大学软件学院,辽宁大连116620

收稿日期:2015-04-21 出版日期:2015-07-23 发布日期:2015-07-28
作者简介:作者简介：申华(1977-),女,河南漯河人,鞍山师范学院数学与信息科学学院讲师，大连理工大学软件学院博士研究生,研究方向：数据挖掘，人工智能。
基金资助:
基金项目：国家自然科学基金资助项目（61272374，61300190）

An Algorithm for Linking Spam Combat on Online Social Networks

1. College of Mathematics and Information Science, Anshan Normal University, Anshan 114005, China；

2. School of Software, Dalian University of Technology, Dalian 116620, China

Received:2015-04-21 Online:2015-07-23 Published:2015-07-28

摘要/Abstract

摘要： 随着社交网络的普遍应用，故意构建大量链接关系以提高自身影响力的作弊行为将给社交网络造成极大的安全隐患。针对这种作弊现象，本文首先提出社交网络用户的4类特征，并利用关系强度模型，提出一种信任和非信任同时双向传播的反作弊改进算法。实验表明采用信任及非信任双向传播的反社交网络链接作弊算法具有良好的对抗性能。

关键词: 关键词：社交网络, 链接作弊, 信任传播, 非信任传播

Abstract: Linking spam on social networks is a process of constructing links among unrelated users to gain the influence. With the extensive application of social networks, linking spam would seriously threaten the information security of social networks. To tackle this problem, this paper proposes four types of users’ features and estimates relationship strength with a graphical model. Further more, propagating algorithm based on both trust and distrust with relationship strength is proposed to combat linking spam on social networks. Experimental results show that the proposed scheme can demote spammers and penalize users that link to spammers effectively.

Key words: Key words: social networks, linking spam, trust propagation； distrust propagation

中图分类号:

TP311.13

申华1,2. 一种对抗社交网络链接作弊的算法#br# [J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2015.07.001.

SHEN Hua1,2. An Algorithm for Linking Spam Combat on Online Social Networks[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.07.001.

参考文献

［1］
Yang Chao, Harkreader R, Gu Guofei, et al. Empirical evaluation and new design for fighting evolving twitter spammers［J］. IEEE Transactions on Information Forensics and Security, 2013,8(8):1280-1293.
［2］ Hu Xia, Tang Jiliang, Gao Huiji, et al. Social spammer detection with sentiment information［C］// IEEE International Conference on Data Mining. 2014:180-189.
［3］ Hu Xia, Tang Jiliang, Liu Huan. Online social spammer detection［C］// Proceedings of the 28th AAAI Conference on Artificial Intelligence.2014:59-65.
［4］ Ghosh S, Viswanath B, Kooti F, et al. Understanding and combating link farming in the twitter social network［C］// Proceedings of the 21st International Conference on World Wide Web. 2012:61-70.
［5］ Ahmed F, Abulaish M. A generic statistical approach for spam detection in online social networks［J］. Computer Communications, 2013,36(1011):1120-1129.
［6］胡俊. 在线社会网络上SPAM行为检测方法研究［D］.武汉：华中科技大学， 2011.
［7］ Yang Chao, Harkreader R, Zhang Jialong, et al. Analyzing spammers’ social networks for fun and profit: A case study of cyber criminal ecosystem on twitter［C］// Proceedings of the 21st International Conference on World Wide Web. 2012:71-80.
［8］ Huang Junxian, Xie Yinglian, Yu Fang, et al. SocialWatch: Detection of online service abuse via largescale social graphs［C］// Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security. ACM, 2013:143-148.
［9］ Shen Hua, Liu Xinyue. Propagating antitrustrank with relationship strength for fighting link farming on twitter［C］// International Seminar on Computation, Communication and Control. 2015:9-12.
［10］马凤龙. Twitter话题影响力用户分析［D］. 大连：大连理工大学， 2013.
［11］Liu Xinyue, Shen Hua, Ma Fenglong, et al. Topical influential user analysis with relationship strength estimation in twitter［C］// IEEE International Conference on Data Mining Workshop. 2014:1012-1019.
［12］McPherson M, SmithLovin L, Cook J M. Birds of a feather: Homophily in social networks［J］. Annual Review of Sociology, 2001,27(1):415-444.
［13］Adali A, Sisenda F, MagdonIsmail M. Actions speak as loud as words: Predicting relationships from social behavior data［C］// Proceedings of the 21st International Conference on World Wide Web. 2012:689-698.
［14］Zhang Xianchao, Wang You, Mo Nan, et al. Propagating both trust and distrust with target differentiation for combating linkbased web spam［C］// Proceedings of the 25th AAAI Conference on Artificial Intelligence. 2011:1292-1297.
［15］Liu Xinyue, Wang You, Zhu Shaoping, et al. Combating Web spam through trustdistrust propagation with confidence［J］. Pattern Recognition Letters, 2013,34(13):1462-1469.
［16］王友. 基于信任和非信任传播的搜索引擎反作弊研究［D］. 大连：大连理工大学， 2011.
［17］Li Rui, Wang Shengjie, Deng Hongbo, et al. Towards social user profiling: Unified and discriminative influence model for inferring homelocations［C］// The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD’12). 2012:1023-1031.

[1]	钟松影. 基于关联规则Apriori算法的纺织原料成本预警[J]. 计算机与现代化, 2023, 0(07): 43-43.
[2]	陈浩, 张亚, 罗希昌, 张亚力, 刘文静. 基于MongoDB的气象数据存储检索系统#br#[J]. 计算机与现代化, 2020, 0(08): 100-104.
[3]	苏林萍，安然，李为，崔文超，张晓良. 基于Hadoop的电力运维审计系统的设计[J]. 计算机与现代化, 2020, 0(01): 49-.
[4]	莫城为，范冰冰. DOP：一种数据开放框架及其应用[J]. 计算机与现代化, 2018, 0(07): 6-.
[5]	季朋1,2，李晖1,2，陈梅1,2，戴震宇1,2. DoFFT：一种基于分布式数据库的快速傅里叶变换方法[J]. 计算机与现代化, 2018, 0(06): 19-.
[6]	陈丽娟，谢伙生. 带负项值的onshelf效用项集并行挖掘算法[J]. 计算机与现代化, 2018, 0(04): 13-.
[7]	刘赛1,聂庆节1,刘军1,李东民2,李静2. 基于量化行为的实时数据库备份系统访问控制模型[J]. 计算机与现代化, 2018, 0(01): 116-122.
[8]	袁兆争1,邵秀丽1,闫凯境2,李丹2，郭建军3. 基于SQL的HBase查询的设计与实现[J]. 计算机与现代化, 2017, 0(7): 20-26+61.
[9]	李金阳，江顺青. 曼哈顿路网中移动数据点的连续概率Skyline查询[J]. 计算机与现代化, 2017, 0(7): 85-90.
[10]	孙笑音，周围. 基于MOOC的大数据分析技术[J]. 计算机与现代化, 2017, 0(4): 89-93,108.
[11]	戴声，王波. 基于图数据库的大规模配置管理数据库关联查询[J]. 计算机与现代化, 2017, 0(1): 51-56.
[12]	张超1,2,郭会明2,张宏2. 基于k均值的监控视频热度云存储研究[J]. 计算机与现代化, 2016, 0(6): 12-15.
[13]	朱茜1,覃华1,冯志新2,陈晨1. 一种大规模文本分类大间隔近邻算法[J]. 计算机与现代化, 2016, 0(6): 68-72.
[14]	吴非,毛宇光. 基于多表关联的关系数据库多空值估计方法[J]. 计算机与现代化, 2016, 0(6): 117-122.
[15]	戴杨,陈芳. 实时数据库中数据的分类压缩算法[J]. 计算机与现代化, 2016, 0(6): 123-126.

一种对抗社交网络链接作弊的算法#br#

An Algorithm for Linking Spam Combat on Online Social Networks

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

本文评价