Investigating cyber alerts with graph-based analytics and narrative visualization

AfzaliSeresht, Neda; Miao, Yuan; Liu, Qing; Teshome, Assefa K.; Ye, Wenjie

doi:10.1109/iv51561.2020.00090

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Cyber-alerts can be investigated efficiently using graph-based analytics and narrative visualization [2]. By capturing complex relationships between alerts and background knowledge in knowledge graphs, security analysts can be assisted with context for interpreting cyberthreats, performing risk management, and achieving a high level of cybersituational awareness.…”

Section: Visualization Of Cyber-knowledge With Knowledge Graphsmentioning

confidence: 99%

“…-Semantic graph databases, including RDF triplestores and quadstores: Allegrograph, 2 Amazon Neptune, 3 AnzoGraph DB, 4 CRAY Graph Engine 5 , Dgraph, 6 GraphDB, 7 MarkLogic, 8 Openlink Virtuoso, 9 RDFox, 10 Stardog 11 -Property graph databases: MEMGraph, 12 NebulaGraph, 13 Neo4j, 14 RedisGraph, 15 TigerGraph, 16 Trovares 17 -Multi-model graph databases: AgensGraph, 18 ArangoDB, 19 DataStax, 20 FlockDB, 21 graphbase.ai, 22 HyperGraphDB, 23 JanusGraph, 24 Microsoft Azure Cosmos DB, 25 Oracle Spatial and Graph, 26 OrientDB, 27 SAP HANA, 28 Sparksee, 29 TerminusDB, 30 TypeDB 31…”

Section: Introduction To Cybersecurity Knowledge Graphsmentioning

confidence: 99%

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Cybersecurity knowledge graphs

Sikos

2023

Knowl Inf Syst

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Cybersecurity knowledge graphs, which represent cyber-knowledge with a graph-based data model, provide holistic approaches for processing massive volumes of complex cybersecurity data derived from diverse sources. They can assist security analysts to obtain cyberthreat intelligence, achieve a high level of cyber-situational awareness, discover new cyber-knowledge, visualize networks, data flow, and attack paths, and understand data correlations by aggregating and fusing data. This paper reviews the most prominent graph-based data models used in this domain, along with knowledge organization systems that define concepts and properties utilized in formal cyber-knowledge representation for both background knowledge and specific expert knowledge about an actual system or attack. It is also discussed how cybersecurity knowledge graphs enable machine learning and facilitate automated reasoning over cyber-knowledge.

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Section: Visualization Of Cyber-knowledge With Knowledge Graphsmentioning

confidence: 99%

Section: Introduction To Cybersecurity Knowledge Graphsmentioning

confidence: 99%

Cybersecurity knowledge graphs

Sikos

2023

Knowl Inf Syst

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Intelligent Financial Risk Warning for Enterprises Through Knowledge Graph-Based Deep Learning

Zheng,

Wu,

Tan

et al. 2024

J CIRCUIT SYST COMP

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The financial risk warning methods for enterprises have always been a practical concern. In digital society, the computational intelligence has brought more spirit to this demand. This paper first introduces the current situation of the development of Knowledge graph technology, describes the deep learning fusion method based on Knowledge graph, and expresses the feasibility of this study. Then, according to the requirements of Knowledge graph, it completes the method fusion of core data training and extraction, and completes the adaptive deep learning design for the Beautiful SCOP database, and establishes a STDE-FG financial risk early warning model. Through empirical analysis, the shortcomings of this model were identified, and a comparison of optimized and optimized results was completed. Two aspects of phenomenon can be found from experimental results. For one thing, the accuracy of the unoptimized STDE-FG early warning model has been improved by 37.5–55.3% compared to traditional prediction models, but the prediction value during legal person changes has a greater error than traditional prediction values. For another, the optimized STDE-FG early warning model has also improved its accuracy in predicting new investments and equity changes, with improvements of 17–32% and 16–28%, respectively, with significant changes. This model will have a positive impact on improving enterprise risk management capabilities and reducing financial risk costs.

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Investigating cyber alerts with graph-based analytics and narrative visualization

Cited by 2 publications

References 17 publications

Cybersecurity knowledge graphs

Cybersecurity knowledge graphs

Intelligent Financial Risk Warning for Enterprises Through Knowledge Graph-Based Deep Learning

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