Resilient distributed resource allocation algorithm under false data injection attacks

TL;DR

This paper presents a resilient distributed resource allocation algorithm for multi-agent systems under false data injection attacks, using extended state observers to estimate and counteract malicious data, ensuring convergence to optimal allocation.

Contribution

It introduces a novel resilient algorithm employing extended state observers to mitigate false data injection attacks without prior attacker information.

Findings

The algorithm successfully maintains system stability under FDI attacks.

It guarantees convergence to optimal resource allocation despite malicious data injections.

The approach is validated through a numerical example.

Abstract

A resilient distributed algorithm is proposed to solve the distributed resource allocation problem of a first-order nonlinear multi-agent system who is subject to false data injection (FDI) attacks. An intelligent attacker injects false data into agents' actuators and sensors such that agents execute the algorithm according to the compromised control inputs and interactive information. The goal of the attacker is to make the multi-agent system to be unstable and to cause the deviance of agents' decisions from the optimal resource allocation. At first, we analyze the robustness of a distributed resource allocation algorithm under FDI attacks. Then, the unknown nonlinear term and the false data injected in agents are considered as extended states which can be estimated by extended state observers. The estimation was used in the feedback control to suppress the effect of the FDI attacks. A resilient distributed resource allocation algorithm based on the extended state observer is proposed to ensure that it can converge to the optimal allocation without requiring any information about the nature of the attacker. An example is given to illustrate the results.