Doctoral Dissertations

Keywords and Phrases

Avionic Systems Security; Cyber-Physical Security; Cyber-Physical Systems; Graph Neural Networks; Integrity; MSDND

Abstract

“Cyber-physical security is a significant concern for critical infrastructures. The exponential growth of cyber-physical systems (CPSs) and the strong inter-dependency between the cyber and physical components introduces integrity issues such as vulnerability to injecting malicious data and projecting fake sensor measurements. Traditional security models partition the CPS from a security perspective into just two domains: high and low. However, this absolute partition is not adequate to address the challenges in the current CPSs as they are composed of multiple overlapping partitions. Information flow properties are one of the significant classes of cyber-physical security methods that model how inputs of a system affect its outputs across the security partition. Information flow supports traceability that helps in detecting vulnerabilities and anomalous sources, as well as helps in rendering mitigation measures.

To address the challenges associated with securing CPSs, two novel approaches are introduced by representing a CPS in terms of a graph structure. The first approach is an automated graph-based information flow model introduced to identify information flow paths in the avionics system and partition them into security domains. This approach is applied to selected aspects of the avionic systems to identify the vulnerabilities in case of a system failure or an attack and provide possible mitigation measures. The second approach is based on graph neural networks (GNN) to classify the graphs into different security domains.

Using these two approaches, successful partitioning of the CPS into different security domains is possible in addition to identifying their optimal coverage. These approaches enable designers and engineers to ensure the integrity of the CPS. The engineers and operators can use this process during design-time and in real-time to identify failures or attacks on the system”--Abstract, page iii.

Advisor(s)

McMillin, Bruce M.

Committee Member(s)

Leopold, Jennifer
Morales, Ricardo
Nadendla, V. Sriram Siddhardh
Kimball, Jonathan W.

Department(s)

Computer Science

Degree Name

Ph. D. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2020

Pagination

xii, 88 pages

Note about bibliography

Includes bibliographic references (pages 82-87).

Rights

© 2020 Anusha Thudimilla, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11760

Electronic OCLC #

1198499047

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