Security analytics on asset vulnerability for information abstraction and risk analysis

Kieran Flanagan, Enda Fallon, Abir Awad, Paul Connolly

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Protecting intellectual property and confidential customer details from network based attacks is becoming increasingly difficult in modern times due to a dramatic increase in online based attacks. For companies such as The NPD Group, protecting this confidential information is key in keeping a positive perceived image while also doing its utmost to protect vital I.P. This paper proposes an architecture that will enable a company to perform a proactive risk assessment of their network to mitigate any possible chance of data leaks or damage to the network. It also performs an abstraction of the performance metrics gained from various data providers to allow for easily understandable metrics pertaining to the risk level of the network at large while also maintaining a level of granularity that can be used by technical experts within the company. SAVIOR is one algorithm within this architecture that uses machine learning mechanisms to perform abstraction of performance metrics gained from a data provider, Nexpose, while also performing an analysis of assets in terms of one area of risk, vulnerability.

Original languageEnglish
Title of host publicationProceedings - 2016 UKSim-AMSS 18th International Conference on Computer Modelling and Simulation, UKSim 2016
EditorsGlenn Jenkins, David Al-Dabass, Alessandra Orsoni, Richard Cant
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9-15
Number of pages7
ISBN (Electronic)9781509008889
ISBN (Print)9781509008889
DOIs
Publication statusPublished - 22 Dec 2016
Event18th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2016 - Cambridge, Cambridgeshire, United Kingdom
Duration: 6 Apr 20168 Apr 2016

Publication series

NameProceedings - 2016 UKSim-AMSS 18th International Conference on Computer Modelling and Simulation, UKSim 2016

Conference

Conference18th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2016
Country/TerritoryUnited Kingdom
CityCambridge, Cambridgeshire
Period6/04/168/04/16

Keywords

  • Machine learning
  • Risk assessment
  • Security
  • Vulnerability analysis

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