Abstract:Open network service is threatened by API abusers such as spammers, phishes, compromised users, etc., because of their open API for any user and third-party developers. In order to preserve the service resource and security, we proposed an approach called CS-1-SVM based on cosine similarity and 1-SVM to detect anomalous accounts who abused API in open network service. Two of the key processes of the method are account modeling and classifier solving. In account modeling, we vectorized every sample user by extracting the dynamic features and calculating the cosine similarity between static features. In classifier solving, we improved 1-SVM in regularization parameter optimization efficiency with cosine similarity too. Based on the proposed method, we developed an experiment to demonstrate that CS-1-SVM has the ability to detect both malicious and compromised account and simplify the process of parameter optimization without reducing the accuracy of 1-SVM.
The Publisher and Editor have retracted this article [1] in accordance with good ethical practices. It was found plagiarised and similar article was published in other journal [2]. The article was published on-line on 14-09-2015.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.