TB-CoAuth: Text based continuous authentication for detecting compromised accounts in social networks

Abstract

Commencement of research towards compromised account detection in email and web services foreshadows the growth of the same in social network scenario. In this paper, continuous authentication of textual content has been performed for incessant authorship verification to detect compromised accounts in social networks. Four categories of features namely, content free, content specific, stylometric and folksonomy are extracted and evaluated. Experiments are performed with 3057 twitter users taking 4000 latest tweets for each user. It is evident from the experiments that consistency maintained on features is different for each user. Hence, various statistical and similarity-based feature selection techniques are used to rank and select optimal features for each user which are further combined using a popular rank aggregation technique called BORDA. Also, performance of various supervised machine learning classifiers is analyzed on the basis of different evaluation metrics. Experimental results state that for the undertaken problem, SVM with rbf kernel outperformed other classifiers namely, kNN, Random Forest, Gradient Boosted and Multi Layer Perceptron, attaining a maximum F-score of 94.57% under the varied parameter settings.