Technology Category
Search and Optimization: Adversarial SearchMultiagent Systems: Adversarial AgentsMachine Learning: Adversarial Learning & Robustness
Efficient Resource Allocation under Adversary Attacks: A Decomposition-Based Approach
🤖 AI Summary
This paper addresses the challenge of allocating limited network resources under persistent, adversarial attacks with unknown statistical properties. Method: We propose a bi-objective optimization framework that jointly minimizes system-wide damage and long-term resource scheduling and inter-node relocation costs. Our approach innovatively integrates chance-constrained programming with dynamic network flow optimization; it decomposes the problem to coordinate node-level defense configuration and global resource allocation, and incorporates online learning for adaptive attack pattern identification. Contribution/Results: We theoretically prove that the method converges to a near-optimal solution without prior knowledge of attack distributions. Experiments across diverse attack intensities and network topologies demonstrate that our method significantly outperforms three baseline strategies—reducing average system damage by 32.7% and operational cost by 28.4%. The framework exhibits strong robustness and practical applicability in dynamic, uncertain adversarial environments.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Allocate limited resources under unknown adversarial attacks
Minimize system damage and resource allocation costs
Propose decomposition-based solution with chance-constrained programming
Innovation
Methods, ideas, or system contributions that make the work stand out.
Decomposition-based bi-objective optimization approach
Chance-constrained programming with network flow
Learning adversarial patterns for resource allocation
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