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In response to the volume and sophistication of malicious software ormalware, security investigators rely on dynamic analysis for malware detectionto thwart obfuscation and packing issues. Dynamic analysis is the process ofexecuting binary samples to produce reports that summarise their runtimebehaviors. The investigator uses these reports to detect malware and attributethreat type leveraging manually chosen features. However, the diversity ofmalware and the execution environments makes manual approaches not scalablebecause the investigator needs to manually engineer fingerprinting features fornew environments. In this paper, we propose, MalDy (mal~die), a portable (plugand play) malware detection and family threat attribution framework usingsupervised machine learning techniques. The key idea of MalDy portability isthe modeling of the behavioral reports into a sequence of words, along withadvanced natural language processing (NLP) and machine learning (ML) techniquesfor automatic engineering of relevant security features to detect and attributemalware without the investigator intervention. More precisely, we propose touse bag-of-words (BoW) NLP model to formulate the behavioral reports.Afterward, we build ML ensembles on top of BoW features. We extensivelyevaluate MalDy on various datasets from different platforms (Android and Win32)and execution environments. The evaluation shows the effectiveness and theportability MalDy across the spectrum of the analyses and settings.

Portable, Data-Driven Malware Detection using Language Processing and Machine Learning Techniques on Behavioral Analysis Reports