In recent years, the security of online social networks (OSNs) has become an issue of widespread concern. Searching and detecting compromised accounts in OSNs is crucial for ensuring the security of OSN platforms. In this study, the authors proposed a new method of detecting compromised accounts based on a supervised analytical hierarchy process (SAHP). First, they considered the expression habits of a user to present the profile features of a user more comprehensively than previous research. Next, the information gain ratio was combined with the analytical hierarchy process algorithm to calculate the weight of each feature. Finally, a detection decision was taken, and varying thresholds were used to obtain different detection results. The experimental results showed that the accuracy and precision of the SAHP were 81.7 and 96.4%, respectively. The results indicated that the new method improved upon the previously established COMPA (detecting compromised accounts on social networks) methods for detecting compromised accounts.

中文翻译：

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基于监督的分析层次结构过程检测在线社交网络的受感染帐户

近年来，在线社交网络（OSN）的安全性已成为广泛关注的问题。在OSN中搜索和检测受感染的帐户对于确保OSN平台的安全性至关重要。在这项研究中，作者提出了一种基于监督分析层次过程（SAHP）的检测受损帐户的新方法。首先，他们考虑了用户的表达习惯以比以前的研究更全面地呈现用户的个人资料特征。接下来，将信息增益比与层次分析算法相结合，以计算每个特征的权重。最后，做出检测决定，并使用不同的阈值获得不同的检测结果。实验结果表明，SAHP的准确度和精确度分别为81.7％和96.4％。