Classifier ensembles using structural features for spammer detection in online social networks

As the online social network technology is gaining all time high popularity and usage, the malicious behavior and attacks of spammers are getting smarter and difficult to track. The newer spamming approaches using the social engineering concepts are making traditional spam and spammer detection techniques obsolete. Especially, content-based filtering of spam messages and spammer profiles in online social networks is becoming difficult. Newer approaches for spammer detection using topological features are gaining attention. Further, the evaluation of ensemble classifiers for detection of spammers over social networking behavior-based features is still in its infancy. In this paper, we present an ensemble learning method for online social network security by evaluating the performance of some basic ensemble classifiers over novel community-based social networking features of legitimate users and spammers in online social networks. The proposed method aims to identify topological and community-based features from users’ interaction network and uses popular classifier ensembles – bagging and boosting to identify spammers in online social networks. Experimental evaluation of the proposed method is done over a real-world data set with artificial spammers that follow a behavior as reported in earlier literature. The experimental results reveal that the identified features are highly discriminative to identify spammers in online social networks.