Targeted Algorithmic Purpose-Driven Cyber Attacks in Distributed Multi-Agent Optimization

TL;DR

This paper introduces purpose-driven cyber attacks targeting distributed multi-agent optimization systems, establishing their theoretical basis and demonstrating their impact through simulations on electric vehicle charging networks.

Contribution

It develops a new class of strategic, purpose-driven algorithmic attacks for DMAO systems, with theoretical foundations and validation through simulations.

Findings

Proposed novel attack strategies that interface with DMAO.

Validated attack effectiveness via simulations on power networks.

Showed potential impact on electric vehicle charging control.

Abstract

Distributed multi-agent optimization (DMAO) enables the scalable control and coordination of a large population of edge resources in complex multi-agent environments. Despite its great scalability, DMAO is prone to cyber attacks as it relies on frequent peer-to-peer communications that are vulnerable to malicious data injection and alteration. Existing cybersecurity research mainly focuses on \emph{broad-spectrum} attacks that aim to jeopardize the overall environment but fail to sustainably achieve specific or targeted objectives. This paper develops a class of novel strategic purpose-driven algorithmic attacks that are launched by participating agents and interface with DMAO to achieve self-interested attacking purposes. Theoretical foundations, in both primal and dual senses, are established for these attack vectors with and without stealthy features. Simulations on electric vehicle charging control validate the efficacy of the proposed algorithmic attacks and show the impacts of such attacks on the power distribution network.