Title

Information Fusion Architecture for Secure Cyber Physical Systems

Abstract

The security essentially governs the usability and stability of Cyber Physical Systems (CPSs) whose cyber and physical components of the system are integrated. Security solutions to this effect are threefold - detecting attacks, preventing them and finally, maintaining system integrity under attacks. In this paper, we provide a solution to the problem of detecting collaborative attacks by proposing an information fusion architecture, which utilizes the strengths of Bayesian estimation in determining causalities as conditional probabilities. We propose a Time-Varying Dynamic Bayesian Network (TVDBN) to ascertain system state information, eventually enabling the system administrator to make control decisions and maintain system stability under security attacks. The control information to the physical components such as actuators is sent over by the sensors, which are the cyber components. As such, the focus of this paper is to provide a solution to uphold the stability of a CPS based on control theoretic aspects that can be adversely affected by compromised cyber components. Theoretical aspects touched in this paper for this purpose are - bounds on transmission delays, quantization errors and sampling time. Using our proposed information fusion architecture, we were able to detect the presence of both, the stand-alone and collaborative attacks on the CPS. Our experimental results showed that our fusion architecture can detect collaborative attacks and profile them with an average accuracy of 91.7%. Given the difficulty in detecting the presence of collaborative attacks in CPS, this level of accuracy is considered high to protect CPS applications.

Department(s)

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Bayesian Networks; Collaborative attacks; Cyber Physical System; Information fusion; Security

International Standard Serial Number (ISSN)

0167-4048

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Elsevier Ltd, All rights reserved.

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