Detecting Fake Accounts in Social Networks Using Principal Components Analysis and Kernel Density Estimation Algorithm (A Case Study on the Twitter Social Network)

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Mohammadrezaei M.R.

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Journal Paper

Paper Information

Journal: JOURNAL OF ELECTRONIC AND CYBER DEFENCE Year:2021 | Volume:9 | Issue:3 Page(s): 109-123

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Information Journal Paper

Title

Detecting Fake Accounts in Social Networks Using Principal Components Analysis and Kernel Density Estimation Algorithm (A Case Study on the Twitter Social Network)

Author(s)

Mohammadrezaei M.R. | Issue Writer Certificate

Keywords

Fake AccountsQ4

Social NetworksQ4

Graph AnalysisQ4

Algorithm Kernel density EstimationQ4

Abstract

The use of Social Networks is growing increasingly and people spend a lot of their time using these networks. Celebrities and companies have used these networks to connect with their fans and customers and news agencies use these networks to publish news. In line with the growing popularity of online Social Networks, security risks and threats are also increasing, and malicious activities and attacks such as phishing, creating Fake Accounts and spam on these networks have increased significantly. In a fake account attack, malicious users introduce themselves instead of other people by creating a fake account and in this way, they abuse the reputation of individuals or companies. This paper presents a new method for detecting Fake Accounts in Social Networks based on machine learning algorithms. The proposed method for machine training uses Various similarity features such as Cosine similarity, Jaccard similarity, friendship network similarity, and centrality measures. All these features are extracted from the graph adjacency matrix of the social network. Then, principal component analysis was used in order to reduce the data dimensions and solve the problem of overfitting. The data are then classified using the Kernel Density Estimation classification and the Self Organization map and the results of the proposed method are evaluated using the measure of accuracy, sensitivity, and false-positive rate. Examination of the results shows that the proposed method detects Fake Accounts with 99. 6% accuracy which is about 5% better than Cao's method. The rate of misdiagnosis of Fake Accounts also improved by 3% compared to the same method.

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