Lightweight Sybil-Resilient Multi-Robot Networks by Multipath Manipulation

TL;DR

ScatterID is a lightweight system that uses backscatter tags to manipulate multipath signals, enabling single-antenna robots to detect Sybil attacks with high accuracy in various environments.

Contribution

The paper introduces ScatterID, a novel, low-cost backscatter-based method for Sybil attack detection in miniaturized robot networks, overcoming the limitations of physical layer signature passivity.

Findings

Achieves an AUROC of 0.988 in detection accuracy.

Attains 96.4% overall identity verification accuracy.

Effective in both indoor and outdoor environments.

Abstract

Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively "observing" signatures, ScatterID actively "manipulates" multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4% for identity verification.