CS-1-SVM: Improved One-class SVM for Detecting API Abuse on Open Network Service

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.

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