An Approach to Discover and Assess Vulnerability Severity Automatically in Cyber-Physical Systems

• 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. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Inbäddad systemteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Embedded Systems (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Nyckelord

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

Distribuerade realtidssystem (DRTS)

Distributed Real-Time Systems

Publikations- och innehållstyp

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