AuthCom: Authorship verification and compromised account detection in online social networks using AHP-TOPSIS embedded profiling based technique. (15th December 2018) - Kaur, Ravneet; Singh, Sarbjeet; Kumar, Harish
Highlights: Analyzed efficiency of authorship verification for detection of compromised accounts. Both content-specific as well as content-free textual features were assessed. Implemented a profiling based technique (later upgraded with AHP-TOPSIS ranking). Assessed how each user maintained consistency in different set of features. Emphasized the fiasco of benchmarking the best set of features. Abstract: In view of the rise in security and privacy concern in social networks, there has been an inadvertent increase in research related to framing of appropriate measures to detect the security breaches in social networks. Cyber criminals are misusing social networking platforms for inappropriate and illegitimate purposes such as posting or sending of illegitimate content which a genuine user will rarely do. Hence, whenever a sensitive and unusual text is posted by a user, there is a need to authenticate whether it is posted by the legitimate owner of the account or some imposter who might have compromised the legitimate profile. The process of authentication called authorship verification helps to handle the same. In this paper, authorship verification has been performed using different textual features such as n-grams, Bag of words (BOW), stylometric and folksonomy features to examine the authorship of tweets posted by the users on the microblogging platform Twitter. Appropriate classification and statistical analysis techniques have been applied to compute different performance parameters. From the experimental analysis, an important observation found is that though char n-grams have an upper hand to other features, still other applicable measures such as word n-grams, BOW, stylometric and folksonomy features cannot be overlooked as each user maintained consistency in different set of features. Accordingly, different feature selection techniques have been used to rank and select best feature for each user. From the comparative analysis of various similarity and statistical based feature selection techniques it is observed that AHP weighted TOPSIS method surpassed others in terms of different performance parameters. Further computation as per ranked features helped to improve the result by achieving an overall average F -score value of 93.82%.