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

Mansoor Davoodi

doi:10.1109/tnsm.2026.3703620

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

2026-01-01

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DOI：10.1109/tnsm.2026.3703620

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Abstract

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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. We further establish an O( root T log(nT)) regret bound, showing that the average root per-round performance gap shrinks as O(1/ T ). 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.

Keywords:

Resource management

Optimization

Modeling

Algorithms

Timing

Costing

Costs

Probability

Fluid flow

Learning (artificial intelligence)

Resource allocation

adversary

decomposition

bi-objective optimization

chance-constrained optimization

network flow

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

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