Detection of compromised accounts for online social networks based on a supervised analytical hierarchy process

Abstract

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.