With the rapid development of the Internet since the beginning of the 21st century, social networks have provided a significant amount of convenience for work, study, and entertainment. Specifically, because of the irreplaceable superiority of social platforms in disseminating information, criminals have thus updated the main methods of social engineering attacks. Detecting abnormal accounts on social networks in a timely manner can effectively prevent the occurrence of malicious Internet events. Different from previous research work, in this work, a method of anomaly detection called Hurst of Interest Distribution is proposed based on the stability of user interest quantifiable from the content of users’ tweets, so as to detect abnormal accounts. In detail, the Latent Dirichlet Allocation model is adopted to classify blog content on Twitter into topics to calculate and obtain the topic distribution of tweets sent by a single user within a period of time. Then, the stability degree of the user’s tweet topic preference is calculated according to the Hurst index to determine whether the account is compromised. Through experiments, the Hurst indexes of normal and abnormal accounts are found to be significantly different, and the detection rate of abnormal accounts using the proposed method can reach up to 97.93%.

中文翻译：

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使用 Hurst of Interest Distribution 检测异常社交网络账户

进入 21 世纪以来，随着互联网的快速发展，社交网络为工作、学习和娱乐提供了极大的便利。具体而言，由于社交平台在传播信息方面具有不可替代的优势，犯罪分子因此更新了社会工程攻击的主要手段。及时发现社交网络异常账​​号，可以有效防止恶意网络事件的发生。与以往的研究工作不同的是，在这项工作中，基于用户推文内容可量化的用户兴趣的稳定性，提出了一种称为Hurst of Interest Distribution的异常检测方法，以检测异常账户。详细，采用Latent Dirichlet Allocation模型对推特上的博客内容进行主题分类，计算并获取一段时间内单个用户发送的推文的主题分布。然后根据Hurst指数计算用户推文主题偏好的稳定度，判断账号是否被盗。通过实验发现，正常账户和异常账户的Hurst指标存在显着差异，使用该方法对异常账户的检出率可达97.93%。