Creating Cybersecurity Knowledge Graphs from Malware After Action Reports

dc.contributor.authorPiplai, Aritran
dc.contributor.authorMittal, Sudip
dc.contributor.authorJoshi, Anupam
dc.contributor.authorFinin, Tim
dc.contributor.authorHolt, James
dc.contributor.authorZak, Richard
dc.date.accessioned2020-01-27T16:29:17Z
dc.date.available2020-01-27T16:29:17Z
dc.date.issued2020-10-6
dc.description.abstractAfter Action Reports provide incisive analysis of cyber-incidents. Extracting cyber-knowledge from these sources would provide security analysts with credible information, which they can use to detect, or find patterns indicative of, a future cyber-attack. It is not possible for a security analyst to read and garner relevant information from a large number of after action reports and similar textual documents that detail an attack. An automated pipeline that extracts from text sources, represents this in a knowledge graph and reasons over it, could help them to analyze cyber-attacks of the future. In this paper, we describe a system to extract information from After Action Reports, which are published by established security corporations, and represent that in a Cybersecurity Knowledge Graph (CKG). We also show how these can also incorporate information from semi structured sources such as STIX. They can also help security analysts execute queries that involve inferences, and retrieve information required to detect a future attack. We extract entities by building a customized named entity recognizer called `Malware Entity Extractor' (MEE). We then build a neural network to predict how pairs of `malware entities' are related to each other. Once, we have predicted entity pairs and the relationship between them, we assert the `entity-relationship set' into a cybersecurity knowledge graph. In this process, each individual source of information (i.e. after action report) would lead to its own graph. Our next step in the process is to fuse the graph on common entities where possible, to create a single graph which represented knowledge in multiple documents. The cybersecurity knowledge graph can be populated from one After Action Report, and can also be fused with another knowledge graph about a similar cyber-attack, or an After Action Reports describing attributes of a similar malware. We show how this knowledge can be used to answer analyst queries that are not possible to be answered from a single source.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/9264152en_US
dc.format.extent13 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2zghw-izgi
dc.identifier.citationAritran Piplai, Sudip Mittal, Anupam Joshi, Tim Finin, James Holt, and Richard Zak. 2019. Creating Cybersecurity Knowledge Graphs from Malware After Action Reports. 16 pages; https://ieeexplore.ieee.org/document/9264152en_US
dc.identifier.urihttp://hdl.handle.net/11603/17071
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.subjectdeep learningen_US
dc.subjectcybersecurityen_US
dc.subjectknowledge graphsen_US
dc.subjectnamed entitiesen_US
dc.subjectmalwareen_US
dc.subjectafter action reportsen_US
dc.titleCreating Cybersecurity Knowledge Graphs from Malware After Action Reportsen_US
dc.typeTexten_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
ACCESS3039234.pdf
Size:
1.1 MB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description: