Abstract:
The increased amount of high-dimensional imbalanced data in online social networks challenges existing feature selection methods. Although feature selection methods such as principal component analysis (PCA) are effective for solving high-dimensional imbalanced data problems, they can be computationally expensive. Hence, an effortless approach for identifying meaningful features that are indicative of anomalous behaviour between humans and malicious bots is presented herein. The most recent Twitter dataset that encompasses the behaviour of various types of malicious bots (including fake followers, retweet spam, fake advertisements, and traditional spambots) is used to understand the behavioural traits of such bots. The approach is based on Benford’s law for predicting the frequency distribution of significant leading digits. This study demonstrates that features closely obey Benford’s law on a human dataset, whereas the same features violate Benford’s law on a malicious bot dataset. Finally, it is demonstrated that the features identified by Benford’s law are consistent with those identified via PCA and the ensemble random forest method on the same datasets. This study contributes to the intelligent detection of malicious bots such that their malicious activities, such as the dissemination of spam, can be minimised.
