COMPA: Detecting Compromised Accounts on Social Networks

By Manuel Egele, Gianluca Stringhini, Christopher Krügel, Giovanni Vigna. 2013

305 citations recorded by the source corpus.

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Citations in the corpus

Practical Evasion of a Learning-Based Classifier: A Case Study (2014, background): “In practice, such assumptions rarely hold, especially for systems deployed online.”
Integro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs (2015, background): “While maintaining an up-to-date ground-truth is important for retraining deployed classifiers and estimating how effective they are in practice, it is rather a time-consuming and nonscalable task.”
EVILCOHORT: Detecting Communities of Malicious Accounts on Online Services (2015, background): “On the other hand, E VIL C OHORT can detect an account (or an IP address) as malicious even if the account does not post any content on the online service.”
CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks (2015, background): “Therefore, recent studies try to detect bot accounts in OSNs by discovering their synchronized group activities [8,11,15,16,31].”
SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection (2013, background): “, front peers) [18], [23], which decreases the number of attack edges and increases the homophily.”
When a Tree Falls: Using Diversity in Ensemble Classifiers to Identify Evasion in Malware Detectors (2016, background): “However, most classifier evasion attempts, including nine targeted mimicry scenarios from two recent studies, are given an outcome of uncertain indicating that these observations cannot be given a reliable prediction by the classifier.”