Integration of Results from Static and Dynamic Code Analysis into an Ontological Model

The use of process automation in malware detection is currently at the forefront of research activities. Machine learning and artificial intelligence appear in the given process as an important part of the solution. To ensure the best possible success we need to provide the best possible information as input. However, these can often only be obtained by combining different types of analysis with different tools. This work presents a system integrating tools for static and dynamic analysis. We aim to integrate the output knowledge from the analyses into one ontological model. We point out the possibilities and pitfalls of integrating knowledge from different sources into a practical implementation. The main advantage is the expansion of knowledge about the given object for other purposes of knowledge processing, whether it is the use of data in the process of machine learning or further interaction with the object.