Detecting compromised social network accounts using deep learning for behavior and text analyses
International Journal of Cloud Applications and Computing
Social networks allow people to connect to one another. Over time, these accounts become an essential part of one’s online identity. The account stores various personal data and contains one’s network of acquaintances. Attackers seek to compromise user accounts for various malicious purposes, such as distributing spam, phishing, and much more. Timely detection of compromises becomes crucial for protecting users and social networks. This article proposes a novel system for detecting compromises of a social network account by considering both post behavior and textual content. A deep multi-layer perceptron-based autoencoder is leveraged to consolidate diverse features and extract underlying relationships. Experiments show that the proposed system outperforms previous techniques that considered only behavioral information. The authors believe that this work is well-timed, significant especially in the world that has been largely locked down by the COVID-19 pandemic and thus depends much more on reliable social networks to stay connected.
Anomaly Score Algorithm, Autoencoder (AE), Behavioral Information, Mean-Squared-Error (MSE), Multi-Layer Perceptron (MLP), Natural Language Processing (NLP), Online Attacks, Textural Information
Steven Yen, Melody Moh, and Teng Sheng Moh. "Detecting compromised social network accounts using deep learning for behavior and text analyses" International Journal of Cloud Applications and Computing (2021): 97-109. https://doi.org/10.4018/IJCAC.2021040106