MADAFE: Malicious Account Detection on Twitter with Automated Feature Extraction

Nowadays, more and more massive cyber-attacks have been launched over social networks. Using compromised or fake accounts, criminals can exploit the inherent trust between connected users to effectively spread malicious content and perform scams against users. Detecting those malicious accounts on social networks like Twitter has received increasing attention from government, industry, and academia. Traditional methods on malicious account detection often leverage features that are created and selected based on domain knowledge of user data, which is inefficient, time-consuming, and possibly biased due to different understanding and observations of the data. In this paper, we propose a new framework, called MADAFE, for accurate and efficient malicious account detection on social networks like Twitter. To overcome the limitation of existing work on manual feature extraction, MADAFE utilizes an autoencoder to automate feature extraction and selection from unlabeled user data. A softmax regression model is also established and trained with the extracted features for classification of benign and malicious accounts. We test MADAFE on different datasets, and extensive simulation results show that MADAFE is effective in detecting malicious accounts, which outperforms state-of-the-art detection methods.