Robust one-shot estimation over shared networks in the presence of denial-of-service attacks

TL;DR

This paper studies secure communication strategies in multi-agent wireless networks under denial-of-service attacks, proposing equilibrium solutions for both proactive and reactive jammers, with practical algorithms for network resilience.

Contribution

It introduces a game-theoretic framework for joint transmission and estimation under jamming, providing saddle point solutions and a sequential convex optimization algorithm.

Findings

Saddle point equilibrium exists for Gaussian observations under proactive jamming.

Sequential convex optimization converges rapidly to Nash equilibria for reactive jamming.

Blocking channels can be optimal for reactive jammers to induce ambiguity.

Abstract

Multi-agent systems often communicate over low-power shared wireless networks in unlicensed spectrum, prone to denial-of-service attacks. We consider the following scenario: multiple pairs of agents communicating strategically over shared communication networks in the presence of a jammer who may launch a denial-of-service. We cast this problem as a game between a coordinator who optimizes the transmission and estimation policies jointly and a jammer who optimizes its probability of performing an attack. We consider two cases: point-to-point channels and large-scale networks with a countably infinite number of sensor-receiver pairs. When the jammer proactively attacks the channel, the game is nonconvex from the coordinator's perspective. However, despite the lack of convexity, we construct a saddle point equilibrium solution for any multi-variate Gaussian distribution for the observations. When the jammer is reactive, we obtain an algorithm based on sequential convex optimization, which converges swiftly to first-order Nash-equilibria. Interestingly, blocking the channel is often optimal when the jammer is reactive, even when it is idle, to create ambiguity at the receiver.