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An Approach to Discover and Assess Vulnerability Severity Automatically in Cyber-Physical Systems
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Distribuerade realtidssystem (DRTS), Distributed Real-Time Systems)ORCID iD: 0000-0003-4791-8452
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Distribuerade realtidssystem (DRTS), Distributed Real-Time Systems)ORCID iD: 0000-0002-7312-9089
2020 (English)In: Proceedings of the 13th International Conference on Security of Information and Networks: November 4-6, 2020, virtual, Istanbul, Turkey / [ed] Berna Örs, Atilla Elçi, New York, NY, USA: Association for Computing Machinery (ACM), 2020, article id 9Conference paper, Published paper (Refereed)
Abstract [en]

Current vulnerability scoring mechanisms in complex cyber-physical systems (CPSs) face challenges induced by the proliferation of both component versions and recurring scoring-mechanism versions. Different data-repository sources like National Vulnerability Database (NVD), vendor websites as well as third party security tool analysers (e.g. ICS CERT and VulDB) may provide conflicting severity scores. We propose a machine-learning pipeline mechanism to compute vulnerability severity scores automatically. This method also discovers score correlations from established sources to infer and enhance the severity consistency of reported vulnerabilities. To evaluate our approach, we show through a CPS-based case study how our proposed scoring system automatically synthesises accurate scores for some vulnerability instances, to support remediation decision-making processes. In this case study, we also analyse the characteristics of CPS vulnerability instances. 

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2020. article id 9
Series
ACM International Conference Proceedings Series (ICPS)
Keywords [en]
Cybersecurity, Text-Mining, Cyber-Physical System, Vulnerability Analysis, CVSS, Decision making, Embedded systems, Turing machines, Current vulnerabilities, Cyber physical systems (CPSs), Data repositories, National vulnerability database, Remediation decision, Scoring systems, Security tools, Third parties, Network security
National Category
Embedded Systems Computer Systems
Research subject
Distributed Real-Time Systems
Identifiers
URN: urn:nbn:se:his:diva-19500DOI: 10.1145/3433174.3433612Scopus ID: 2-s2.0-85100625302ISBN: 978-1-4503-8751-4 (print)OAI: oai:DiVA.org:his-19500DiVA, id: diva2:1531138
Conference
13th International Conference on Security of Information and Networks, SIN 2020, November 4-6, 2020, virtual, Istanbul, Turkey
Note

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, or republish,to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from permissions@acm.org.SIN 2020, November 4–7, 2020, Merkez, Turkey© 2020 Association for Computing Machinery.

Available from: 2021-02-25 Created: 2021-02-25 Last updated: 2021-08-20Bibliographically approved

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Jiang, YuningAtif, Yacine

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