Understanding the information in captured network traffic, extracting the necessary data, and performing incident investigations are principal tasks of network forensics. The analysis of such data is typically performed by tools allowing manual browsing, filtering, and aggregation or tools based on statistical analyses and visualizations facilitating data comprehension. However, the human brain is used to perceiving the data in associations, which these tools can provide only in a limited form. We introduce a GRANEF toolkit that demonstrates a new approach to exploratory network data analysis based on associations stored in a graph database. In this article, we describe data transformation principles, utilization of a scalable graph database, and data analysis techniques. We then discuss and evaluate our proposed approach using a realistic dataset. Although we are at the beginning of our research, the current results show the great potential of association-based analysis.