“Cybersecurity Knowledge Graph Improvement with Graph Neural Networks”

First authored paper accepted to the BigCyber Conference @ IEEE Big Data 2021.

Cybersecurity Knowledge Graphs (CKGs) help in aggregating information about cyber-events. CKGs have assertions in the form of semantic triples. The triples describe a relationship between a subject and object, both of which are cybersecurity entities. In this paper, we describe a novel method that associates a score with the semantic triples asserted in the CKG using deep learning. We use semantic triples that we know are correct, in a supervised machine learning algorithm that produces the output for each relationship. In particular, we use Graph Convolutional Neural Networks (GCN) on a dataset of CKGs that can be used to ascertain the scores for each semantic triple.

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Recommended citation:

Dasgupta, Soham, et al. “Cybersecurity Knowledge Graph Improvement with Graph Neural Networks.” 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021.