在线社交网络恶意信息多源定位算法

doi:10.19678/j.issn.1000-3428.0051787

计算机工程 ›› 2019, Vol. 45 ›› Issue (9): 119-123. doi: 10.19678/j.issn.1000-3428.0051787

在线社交网络恶意信息多源定位算法

袁得嵛^1,2, 黄淑华^1,2, 叶萌熙¹, 王小娟³

1. 中国人民公安大学信息技术与网络安全学院, 北京 102623;
2. 安全防范技术与风险评估公安部重点实验室, 北京 102623;
3. 北京邮电大学电子工程学院, 北京 100876

收稿日期:2018-06-11 修回日期:2018-08-28 出版日期:2019-09-15 发布日期:2019-09-03
作者简介:袁得嵛(1986-),男,讲师、博士,主研方向为网络安全、复杂网络;黄淑华,副教授、硕士;叶萌熙,本科生;王小娟,副教授、博士。
基金资助:
国家重点研发计划（2017YFC0803700）；国家自然科学基金面上项目"未来超密集异构网络的理论分析与资源协同优化研究"（61771072）；北京市自然科学基金（4184099）；公安部科技强警基础工作专项（2017GABJC38）；中国人民公安大学基本科研业务费专项资金（2016JKF01317）。

Multi-Source Location Algorithm for Malicious Information in Online Social Network

YUAN Deyu^1,2, HUANG Shuhua^1,2, YE Mengxi¹, WANG Xiaojuan³

1. School of Information Technology and Cyber Security, People's Public Security University of China, Beijing 102623, China;
2. Key Laboratory of Safety Precaution Technology and Risk Assessment, Ministry of Public Security, Beijing 102623, China;
3. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2018-06-11 Revised:2018-08-28 Online:2019-09-15 Published:2019-09-03
Supported by:
This work is supported by the Yong Talents Research Project of SGERI (No.XM2018020165196).

摘要/Abstract

摘要： 针对恶意信息源覆盖范围重叠导致基于全网拓扑的定位算法复杂度高的情况，提出基于社区结构的子图划分算法，将恶意信息多源定位问题分解为多个单源定位问题。在此基础上，利用基于Jordan中心的在线社交网络多源定位算法，实现多个子图内的恶意信息单源定位。在随机数网络和UCIonline网络上的仿真结果表明，该算法能够有效识别恶意信息源，定位准确率相比基于距离中心、紧密度中心和介数中心的算法提高11%~30%。

关键词: 在线社交网络, 恶意信息, 子图划分, Jordan中心, 多源定位

Abstract: To address the high complexity of the location algorithm based on the topology of the whole network,which is led by a coverage overlap between malicious information sources,a subgraph division algorithm based on community structure is proposed.The algorithm decomposes the location problem of multi-source malicious information into multiple single-source location problems.On this basis,the multi-source location algorithm for Online Social Network(OSN) based on the Jordan center is used for the single-source malicious information location in multiple subgraphs.Simulation results on the random number network and UCIonline network show that the algorithm can effectively identify malicious information sources,and its location accuracy is 11%~30% higher than that of algorithm based on Distance Center(DC),Tightness Center(TC) and Betweenness Center(BC).

Key words: Online Social Network(OSN), malicious information, subgraph division, Jordan center, multi-source location

中图分类号:

TP399

袁得嵛, 黄淑华, 叶萌熙, 王小娟. 在线社交网络恶意信息多源定位算法[J]. 计算机工程, 2019, 45(9): 119-123.

YUAN Deyu, HUANG Shuhua, YE Mengxi, WANG Xiaojuan. Multi-Source Location Algorithm for Malicious Information in Online Social Network[J]. Computer Engineering, 2019, 45(9): 119-123.

https://www.ecice06.com/CN/Y2019/V45/I9/119

图/表 6

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