Affine Transformation-based Perfectly Undetectable False Data Injection Attacks on Remote Manipulator Kinematic Control with Attack Detector

TL;DR

This paper introduces a method for executing perfectly undetectable affine transformation attacks on robotic manipulators, allowing attackers to manipulate trajectories without detection, validated through experiments on a FANUC robot.

Contribution

It presents a novel affine transformation attack framework that remains completely hidden from detection systems, demonstrated on real robotic manipulators.

Findings

Attack scenarios remain undetected with similar end effector errors as nominal cases

Affine transformation attacks can manipulate trajectories via scaling, reflection, and shearing

Experimental validation confirms attack effectiveness on a real FANUC robot

Abstract

This paper demonstrates the viability of perfectly undetectable affine transformation attacks against robotic manipulators where intelligent attackers can inject multiplicative and additive false data while remaining completely hidden from system users. The attacker can implement these communication line attacks by satisfying three Conditions presented in this work. These claims are experimentally validated on a FANUC 6 degree of freedom manipulator by comparing a nominal (non-attacked) trial and a detectable attack case against three perfectly undetectable trajectory attack Scenarios: scaling, reflection, and shearing. The results show similar observed end effector error for the attack Scenarios and the nominal case, indicating that the perfectly undetectable affine transformation attack method keeps the attacker perfectly hidden while enabling them to attack manipulator trajectories.