Using A Malware Ontology To Construct A Malware Knowledge Graph In this study, we reviewed and extended an existing malware ontology to cover android malware. our extended ontology is called andmalont. it consisted of 13 new classes, 16 object properties, and 31 data properties. In this paper, we introduce an open source malware ontology malont that allows the structured extraction of information and knowledge graph generation, especially for threat intelligence.
Using A Malware Ontology To Construct A Malware Knowledge Graph In this paper, we introduce an open source malware ontology, malont that allows the structured extraction of information and knowledge graph generation, especially for threat intelligence. Given that ontology techniques are useful to express the domain knowledge meaningfully, this paper aims to develop an ontology for dynamic analysis of malware behaviour and to capture metamorphic and polymorphic malware behaviour. This ontology forms the basis for the malware threat intelligence knowledge graph, malkg, which we exemplify using three different, non overlapping demonstrations. This paper proposed a method for constructing malware knowledge graphs based on a joint extraction model. firstly, a malware ontology model was proposed for threat intelligence.
Malware Detection And Classification Based On Graph Convolutional This ontology forms the basis for the malware threat intelligence knowledge graph, malkg, which we exemplify using three different, non overlapping demonstrations. This paper proposed a method for constructing malware knowledge graphs based on a joint extraction model. firstly, a malware ontology model was proposed for threat intelligence. In our work, we study how to use ontology to represent the knowledge of malware individuals and families, and how to build the framework of malware knowledge base. In this paper, we introduce an open source malware ontology, malont that allows the structured extraction of information and knowledge graph generation, especially for threat intelligence. This initiative focuses on the creation of a comprehensive knowledge graph from detailed malware analysis reports. this graph not only categorizes malware instances but also connects them to related threat actors and campaigns, revealing the broader narrative of cyber threats. An existing malware ontology is reviewed and extended to cover android malware to ensure consistency in representing concepts and entities across various sources and creates an android malware knowledge graph that encompasses over 2600 malware samples.
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