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In this work we propose a graph-based model that, utilizing relations betweengroups of System-calls, distinguishes malicious from benign software samplesand classifies the detected malicious samples to one of a set of known malwarefamilies. More precisely, given a System-call Dependency Graph (ScDG) thatdepicts the malware's behavior, we first transform it to a more abstractrepresentation, utilizing the indexing of System-calls to a set of groups ofsimilar functionality, constructing thus an abstract and mutation-tolerantgraph that we call Group Relation Graph (GrG); then, we construct another graphrepresentation, which we call Coverage Graph (CvG), that depicts the dominatingrelations between the nodes of a GrG graph. Based on the research so far in thefield, we pointed out that behavior-based graph representations had notleveraged the aspect of the temporal evolution of the graph. Hence, the noveltyof our work is that, preserving the initial representations of GrG and CvGgraphs, we focus on augmenting the potentials of theses graphs by addingfurther features that enhance its abilities on detecting and furtherclassifying to a known malware family an unknown malware sample. To that end,we construct periodical instances of the graph that represent its temporalevolution concerning its structural modifications, creating another graphrepresentation that we call Temporal Graphs. In this paper, we present thetheoretical background behind our approach, discuss the current technologicalstatus on malware detection and classification and demonstrate the overallarchitecture of our proposed detection and classification model alongside withits underlying main principles and its structural key-components.

Malicious Software Detection and Classification utilizing Temporal-Graphs of System-call Group Relations