Early identification of spammers through identity linking, social network and call features

摘要

Multiple identities are created to gain financial benefits by performing malicious activities such as spamming, committing frauds and abusing the system. A single malicious individual may have a large number of identities in order to make malicious activities to a large number of legitimate individuals. Linking identities of an individual would help in protecting the legitimate users from abuses, frauds, and maintains reputation of the service provider. Simply analyzing each identity's historical behavior is not sufficient to block spammers frequently changing identity because spammers quickly discards the identity and start using new one. Moreover, spammers may appear as a legitimate user on an initial analysis, for example because of small number of interactions from any identity. The challenge is to identify the spammer by analyzing the aggregate behavior of an individual rather than that of a single calling identity. This paper presents EIS (early identification of spammers) system for the early identification of spammers frequently changing identities. Specifically, EIS system consists of three modules and uses social call graph among identities. (1) An ID-CONNED' module that links identities that belongs to a one physical individual based on a social network structure and calling attributes of identities; (2) a reputation module that computes reputation of an individual by considering his aggregate behavior from his different identities; and (3) a detection module that computes automated threshold below which individuals are classified as a spammer or a non-spammer. We evaluate the proposed system on a synthetic data-set that has been generated for the different graph networks and different percentage of spammers. Performance analysis shows that EIS is effective against spammers frequently changing their identities and is able to achieve high true positive rate when spammers have high small overlap in target victims from their identities. (C) 2016 Elsevier B.V. All rights reserved.