Detecting malicious reviews and users affecting social reviewing systems: A survey

Abstract

The proliferation of attacks on On-line Social Networks (OSNs) has imposed particular attention by providers and users. This has an even higher importance for Social Reviewing Systems (SRSs), where users can be strongly conditioned by means of malevolent reviews and behavior of fake or camouflage accounts. There is a rich literature on the use of strong authentication means, encryption or privacy-preserving schemes for OSNs and SRSs, but these mechanisms only represent a first defense line. Advanced attacks may be able to bypass such a defense and to successfully threaten the system and harm its users. Therefore, it is needed to embed a second defence line to peculiar attack scenarios of SRSs that take advantage of user dynamics occurring within social networks. This survey focuses on the issue and solutions to detect malicious reviews and users so as to exclude them from social networks, protect legitimate and honest users and keep the credibility of the protected SRS as high as possible. Therefore, we provide an evaluation of the main detection solutions for the mentioned specific attacks against SRSs in according to different metrics on several standard datasets.