CAMP: Content-Agnostic Malware Protection

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When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks (2018, background): “The ML-based systems employed in the security industry [21, 10, 39, 12], often rely on undisclosed features to render attacks more difficult, thus constraining the F”
Practical Evasion of a Learning-Based Classifier: A Case Study (2014, background): “Examples of such attacks have been previously studied under the assumption that an attacker has full knowledge about the deployed classifier.”
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.”
Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data (2014, background): “CAMP: content-agnostic malware protection.”
Cracking Classifiers for Evasion: A Case Study on the Google's Phishing Pages Filter (2016, unknown): “Various classifiers were trained for detecting malicious web pages [25], spam [54], phishing [58], malware [47], etc.”