Enhancing spammer detection in online social networks with trust-based metrics.

As online social networks acquire larger user bases, they also become more interesting targets for spammers. Spam can take very different forms on social Web sites and cannot always be detected by analyzing textual content. However, the platform\u27s social nature also offers new ways of approaching the spam problem. In this work the possibilities of analyzing a user\u27s direct neighbors in the social graph to improve spammer detection are explored. Special features of social Web sites and their implicit trust relations are utilized to create an enhanced attribute set that categorizes users on the Twitter microblogging platform as spammers or legitimate users

On-the-fly Intrusion Detection for Web Portals

Remote access to distributed hyper-linked information proves to be one of the killer applications for computer networks. More and more content in current inter and intra nets is available as hyper-data, a form easing its distribution and semantic organization