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Dynamic and Automatic Vulnerability Assessment for Cyber-Physical System
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Distribuerade realtidssystem (DRTS), Distributed Real-Time Systems)ORCID iD: 0000-0003-4791-8452
2019 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Assessing vulnerabilities supports analytics-based decision-making processes to protect Critical Infrastructures (CIs), in order to focus on specific risks rising from threat-exploitability with varying degrees of impact-severity. The notion of risk remains elusive, as evidenced by the increasing investigations on CIs security operations centres (SOCs) where analysts employ various detection, assessment, and defence mechanisms to monitor security events. Normally, SOCs involve advances of multiple automated security tools such as network vulnerability scanners and Common Vulnerability Scoring System (CVSS), combined with analysis of data contained and produced by cyber-physical system (CPS) as well as alarms retrieved from vulnerability repositories such as Common Vulnerability Exposure (CVE). The security operators need further to forecast the match between these vulnerabilities and the state of intricate CIs layer networks, while prioritising patching investments using vulnerability-scoring mechanisms. This process shows the central role of security operators in SOCs and their need for support to keep pace with dynamically evolving vulnerability-alert repositories. Recent advances in data analytics also prompt dynamic data-driven vulnerability assessments whereby data contained and produced by CPS include hidden traces of vulnerability fingerprints. However, the huge volume of scanned data requires high capability of information processing and analytical reasoning, which could not be satisfied considering the imprecise nature of manual vulnerability assessment.

A knowledge-base system that consolidates both sides into empirical rules appears to be missing, yet it promises to offer a suitable level of decision-support. In our research, we propose a dynamic and automated vulnerability-assessment approach. The proposed streamlined approach employs computational intelligence techniques to analyse data retrieved from vulnerability-alert repositories and CPS layer networks within an innovative accurate and automatic scoring system, away from traditional manual and highly subjective mechanisms. Our approach suggests to substitute offline, costly, error-prone and pure subjective vulnerability assessment processes with an automatic, accurate and data-evidenced approach, to improve situation awareness and to support security decision making. In doing so, we investigate judicious computational-intelligence techniques such as fuzzy-logic, machine learning and data mining, applied to vulnerability assessment problems.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Cyber-Physical System Security, Vulnerability Assessment
National Category
Embedded Systems Computer Sciences
Research subject
Distributed Real-Time Systems
Identifiers
URN: urn:nbn:se:his:diva-17752OAI: oai:DiVA.org:his-17752DiVA, id: diva2:1357177
Conference
19th Seminar within the Framework of a Swedish IT Security Network for PhD students, Karlstad, Sweden, June 3-4, 2019
Projects
ELVIRA
Note

A short presentation was given during SWITS'2019 workshop for the poster.

Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-04Bibliographically approved

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Jiang, Yuning

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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