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Detecting the newly emerging malware variants in real time is crucial formitigating cyber risks and proactively blocking intrusions. In this paper, wepropose MG-DVD, a novel detection framework based on dynamic heterogeneousgraph learning, to detect malware variants in real time. Particularly, MG-DVDfirst models the fine-grained execution event streams of malware variants intodynamic heterogeneous graphs and investigates real-world meta-graphs betweenmalware objects, which can effectively characterize more discriminativemalicious evolutionary patterns between malware and their variants. Then,MG-DVD presents two dynamic walk-based heterogeneous graph learning methods tolearn more comprehensive representations of malware variants, whichsignificantly reduces the cost of the entire graph retraining. As a result,MG-DVD is equipped with the ability to detect malware variants in real time,and it presents better interpretability by introducing meaningful meta-graphs.Comprehensive experiments on large-scale samples prove that our proposed MG-DVDoutperforms state-of-the-art methods in detecting malware variants in terms ofeffectiveness and efficiency.

MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning