This document discusses modeling and optimizing the performance-security tradeoff in distributed networked control systems (D-NCS) using a coevolutionary genetic algorithm (CGA). It presents a tradeoff model for a system's dynamic performance and its security. The CGA is proposed as a paradigm to optimize this performance-security tradeoff for D-NCS. A Simulink-based testbed demonstrates the effectiveness of using the CGA to efficiently find optimal values in the performance-security tradeoff model for D-NCS.

1. MODELING AND OPTIMIZING THE PERFORMANCE- SECURITY TRADEOFF ON
D-NCS USING THE COEVOLUTIONARY PARADIGM
ABSTRACT:
Distributed networked control systems (D-NCS) are vulnerable to various network attacks when
the network is not secured; thus, D-NCS must be well protected with security mechanisms (e.g.,
cryptography), which may adversely affect the dynamic performance of the D-NCS because of
limited system resources.
This paper addresses the tradeoff between D-NCS security and its real-time performance and
uses the Intelligent Space (iSpace) for illustration. A tradeoff model for a system’s dynamic
performance and its security is presented. This model can be used to allocate system resources to
provide sufficient protection and to satisfy the D-NCS’s real-time dynamic performance
requirements simultaneously.
We propose a paradigm of the performance-security tradeoff optimization based on the
coevolutionary genetic algorithm (CGA) for D-NCS. A Simulink-based test-bed is implemented
to illustrate the effectiveness of this paradigm. The results of the simulation show that the CGA
can efficiently find the optimal values in a performance-security tradeoff model for D-NCS.
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