CNN-based malicious user detection in social networks

摘要

Following the advances in various smart devices, there are increasing numbers of users of social network services (SNS), which allows communication and information sharing in real time without limitations on distance or space. Although personal information leakage can occur through SNS, where an individual's personal details or online activities are leaked, and various financial crimes such as phishing and smishing are also possible, there are currently no countermeasures. Consequently, malicious activities are being conducted through messages toward the users who are in follow or friend relationships on SNS. Therefore, in this paper, we propose a method of assessing follow suggestions from users with less likelihood of committing malicious activities through an information-driven follow suggestion based on a categorical classification of interests using both the images and text of user posts. We ensure the objectiveness of interest categories by defining these based on DMOZ, which is established by the Open Directory Project. The images and text are learnt using a convolutional neural network, which is one of the machine learning techniques developed with a biological inspiration, and the interests are classified into categories. Users with a large number of posts are defined as certified users, and a database of certified users is established. Users with similar interests are classified, and the similarity distances between certified users and users are measured, and a follow suggestion is generated to the certified user with the most similar interest. Using the method proposed in this paper to classify the interest categories of certified users and users, precisions of 80% and 79.8% were obtained, respectively, and the overall precision was 79.93%, indicating a good classification performance overall. It is expected that the method proposed in this paper can be used to provide follow suggestions of users with less likelihood of malicious activities based on the information posted by the user.