基于改进边权重的成对马尔可夫随机场模型的社交异常账号检测方法

社交媒体系统为人们提供了便利的共享、交流和协作平台。人们在享受社交媒体的开放性和便利性时,可能会发生许多恶意行为,例如欺凌、恐怖袭击计划和欺诈信息传播。因此,尽可能准确、及早地发现这些异常活动,以防止灾难和袭击,是非常重要的。近年来,随着在线社交网络(OSN)如Twitter,Facebook,Google+,LinkedIN等的成功,丰厚的利益资源使得它们成为了攻击者的目标。社交网络的开放性,使其特别容易受到异常账号攻击的威胁。现有基于图形的最先进分类模型大多使用首先为图的边分配权重,在加权图中迭代地传播节点的信誉分数,并使用最终的后验分数来对节点进行分类的方法。边权重的分配是其中一项重要的任务,此参数将直接影响检测结果的准确度。为此,文中针对社交媒体中异常账号的检测任务,分析了基于社交图全局结构的方法,通过在成对马尔可夫随机场模型中改进边权重的计算方法,使其能够在迭代过程中自适应优化,提出了准确度更高的GANG+LW,GANG+LOGW和GANG+PLOGW算法。这3种算法使用了不同的改进边权重的方法。实验证明,新提出的方法相对于基本的成对马尔可夫随机场模型,可取得更准确的异常账号检测结果,3种算法中GANG+PLOGW得到的结果最好。结果证明,此改进模型在检测社交网络中的异常账号时,能够更有效地解决问题。

Fake Account Detection Method in Online Social Network Based on Improved Edge Weighted Paired Markov Random Field Model

Social media systems provide a convenient platform for sharing,communication and collaboration.When people enjoy the openness and convenience of social media,there may be many malicious acts,such as bullying,terrorist attacks and fraudulent information dissemination.Therefore,it is very important to be able to detect these anomalous activities as accurately and early as possible to prevent disasters and attacks.The success of online social networks(OSN)in recent years,such as Twitter,Facebook,Google+,LinkedIn,has made them targests of attacker’s goal due to their rich profit resources.The openness of social networks makes them particularly vulnerable to unusual account attacks.Existing classification models mostly use method that first assign weights to the edges of the graph,iteratively propagate the reputation scores of the nodes in the weighted graph,and use the final posterior scores to classify the nodes.One of the important tasks is the settingof edge weight.This parameter will directly affect the accuracy of the test results.Based on the detection task of fake account in social media,this paper analyzed the global structure based on social graph,and improves the algorithm of edge weight in the paired Markov random field model,so that it can adaptively optimize in the iterative process.GANG+LW,GANG+LOGW,and GANG+PLOGW algorithms with higher accuracy were proposed.These three algorithms used three different methods to improve the algorithm of edge weight.Experiments show that the proposed method can obtain more accurate fake account detection results than the basic paired Markov random field model,in which GANG+PLOGW got the best results in the three algorithms.The result proves that this improved model can solve the problem more effectively when detecting fake accounts in social networks.