Dasgupta, SohamPiplai, AritranRanade, PriyankaJoshi, Anupam2022-02-072022-02-072022-01-13S. Dasgupta, A. Piplai, P. Ranade and A. Joshi, "Cybersecurity Knowledge Graph Improvement with Graph Neural Networks," 2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 3290-3297, doi: 10.1109/BigData52589.2021.9672062.https://doi.org/10.1109/BigData52589.2021.9672062http://hdl.handle.net/11603/241272021 IEEE International Conference on Big Data (Big Data)Cybersecurity Knowledge Graphs (CKGs) help in aggregating information about cyber-events. CKGs combined with reasoning and querying systems such as SPARQL enable security researchers to look up information about past cyberevents that is helpful in understanding future cyber-events or drawing similarity with a known cyber-event recorded in a CKG. 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. The quality of information present in the CKG depends on the data source. Since data sources can have varying degrees of reliability, we need a score that should help us benchmark the veracity of the CKG assertions. Verifying the information asserted in the CKG is a challenging task. 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.8 pagesen-US© 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.UMBC Ebiquity Research GroupText