# Secure Fusion with Labeled Multi-Bernoulli Filter for Multisensor Multitarget Tracking Against False Data Injection Attacks

## TL;DR

This paper introduces a method to track multiple targets using multiple sensors while defending against false data injection attacks.

## Contribution

A novel algorithm combining labeled multi-Bernoulli filtering and Kullback–Leibler divergence for detecting and defending against FDI attacks in multisensor multitarget tracking.

## Key findings

- The proposed algorithm efficiently detects false data injection attacks using Kullback–Leibler divergence between labeled multi-Bernoulli densities.
- The method provides reliable tracking performance even when sensor networks are compromised.
- A Gaussian implementation of the algorithm is presented for linear Gaussian models.

## Abstract

What are the main findings?

A multisensor multitarget tracking algorithm against the false data injection (FDI) attacks over networks.A detection method for FDI attacks based on Kullback–Leibler divergence (KLD) between labeled multi-Bernoulli densities.

A multisensor multitarget tracking algorithm against the false data injection (FDI) attacks over networks.

A detection method for FDI attacks based on Kullback–Leibler divergence (KLD) between labeled multi-Bernoulli densities.

What is the implication of the main finding?

The proposed algorithm can efficiently detect/defend the FDI attacks and provide reliable tracking performance.

The proposed algorithm can efficiently detect/defend the FDI attacks and provide reliable tracking performance.

This paper addresses multisensor multitarget tracking where the sensor network can potentially be compromised by false data injection (FDI) attacks. The existence of the targets is not known and time-varying. A tracking algorithm is proposed that can detect the possible FDI attacks over the networks. First, a local estimate is generated from the measurements of each sensor based on the labeled multi-Bernoulli (LMB) filter. Then, a detection method for FDI attacks is derived based on the Kullback–Leibler divergence (KLD) between LMB random finite set (RFS) densities. The statistical characteristics of the KLD are analyzed when the measurements are secure or compromised by FDI attacks, from which the value of the threshold is selected. Finally, the global estimate is obtained by minimizing the weighted sum of the information gains from all secure local estimates to itself. A set of suitable weight parameters is selected for the information fusion of LMB densities. An efficient Gaussian implementation of the proposed algorithm is also presented for the linear Gaussian state evolution and measurement model. Experimental results illustrate that the proposed algorithm can provide reliable tracking performance against the FDI attacks.