Sybil Account Detection in Online Social Networks Using Statistical Feature Selection Techniques

Machine Learning (ML) research greatly helps in predicting model-based outcomes with high levels of accuracy based upon the training and testing of the models through the datasets. The social networks constitute one of the domains where ML can be used effectively to ensure the authenticity and security of the valid users. With the increase in usage of Online Social Networks (OSNs), the cases of spam and malicious activities can be found in abundance and Sybil nodes pose one such kind of safety and security hazard. Sybil account detection is not an easy task since they mimic the actual behavior of human accounts up to a great extent. In this paper, we look at one such scenario of Sybil accounts on the OSN, Twitter where machine leaning models have been used to train the machine with the existing datasets so as to be able to detect these malicious users before they can bring harm to the normal communication of the genuine users. Since the datasets used are so vast, the process of feature selection has been carried on the datasets as part of pre-processing before the actual classification as it assists in enhancing the model performance. Support Vector Machine–Recursive Feature Elimination (SVM-RFE) and Logistic Regression–Recursive Feature Elimination (LR-RFE) techniques have been used in this study for the selection of significant features. The classification model is trained on the selected features using Random Forest (RF) and K-Nearest Neighbor (KNN) algorithms. We also analyzed the biasing effects of fake accounts on the human accounts datasets during the process of features selection and classification. It has been shown that the RF algorithm outperformed KNN on the feature sets selected through SVM-RFE and LR-RFE.

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Spam profiles detection on social networks using computational intelligence methods: The effect of the lingual context

Journal of Information Science ◽

10.1177/0165551519861599 ◽

2019 ◽

pp. 016555151986159 ◽

Cited By ~ 1

Author(s):

Ala’ M Al-Zoubi ◽

Ja’far Alqatawna ◽

Hossam Faris ◽

Mohammad A Hassonah

Keyword(s):

Social Networks ◽

Feature Selection ◽

Online Social Networks ◽

Information Gain ◽

Detection Methods ◽

Spam Detection ◽

Selection Methods ◽

Chi Square ◽

Computational Intelligence Methods ◽

Independent Features

In online social networks, spam profiles represent one of the most serious security threats over the Internet; if they do not stop producing bad advertisements, they can be exploited by criminals for various purposes. This article addresses the nature and the characteristics of spam profiles in a social network like Twitter to improve spam detection, based on a number of publicly available language-independent features. In order to investigate the effectiveness of these features in spam detection, four datasets are extracted for four different language contexts (i.e. Arabic, English, Korean and Spanish), and a fifth is formed by combining them all. We conduct our experiments using a set of five well-known classification algorithms in spam detection field, k-Nearest Neighbours ( k-NN), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT) (J48) and Multilayer Perceptron (MLP) classifiers, along with five filter-based feature selection methods, namely, Information Gain, Chi-square, ReliefF, Correlation and Significance. The results show oscillating performance of each classifier across all datasets, but improved classification results with feature selection. In addition, detailed analysis and comparisons are carried out on two different levels: in the first level, we compare the selected features’ importance among the feature selection methods, whereas in the second level, we observe the relations and the importance of the selected features across all datasets. The findings of this article lead to a better understanding of social spam and improving detection methods by considering the various important features resulting from the different lingual contexts.

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