Efficient Resource Allocation under Adversary Attacks: A Decomposition-Based Approach

TL;DR

This paper presents a decomposition-based method for resource allocation in networks under adversarial attacks, optimizing damage reduction and cost efficiency without prior knowledge of attack patterns.

Contribution

It introduces a novel bi-objective optimization framework combining chance constraints and network flow, with proven convergence to optimal solutions under unknown adversarial behavior.

Findings

Method effectively learns adversarial patterns.

Achieves significant damage and cost reduction.

Outperforms benchmark strategies in simulations.

Abstract

We address the problem of allocating limited resources in a network under persistent yet statistically unknown adversarial attacks. Each node in the network may be degraded, but not fully disabled, depending on its available defensive resources. The objective is twofold: to minimize total system damage and to reduce cumulative resource allocation and transfer costs over time. We model this challenge as a bi-objective optimization problem and propose a decomposition-based solution that integrates chance-constrained programming with network flow optimization. The framework separates the problem into two interrelated subproblems: determining optimal node-level allocations across time slots, and computing efficient inter-node resource transfers. We theoretically prove the convergence of our method to the optimal solution that would be obtained with full statistical knowledge of the adversary. Extensive simulations demonstrate that our method efficiently learns the adversarial patterns and achieves substantial gains in minimizing both damage and operational costs, comparing three benchmark strategies under various parameter settings.