An Iterative Security Game for Computing Robust and Adaptive Network Flows

TL;DR

This paper introduces an iterative game-based model for securing network flows against adversarial attacks, focusing on robustness and adaptiveness in uncertain environments, with practical heuristics and extensive empirical validation.

Contribution

It proposes a novel iterative two-player game framework for robust network flow optimization under attack, including heuristic solutions and real-world data testing.

Findings

Robust flow solutions increase network throughput significantly.

The approach reduces expected flow loss compared to benchmarks.

Heuristics enable scalable solutions for large urban networks.

Abstract

The recent advancement in real-world critical infrastructure networks has led to an exponential growth in the use of automated devices which in turn has created new security challenges. In this paper, we study the robust and adaptive maximum flow problem in an uncertain environment where the network parameters (e.g., capacities) are known and deterministic, but the network structure (e.g., edges) is vulnerable to adversarial attacks or failures. We propose a robust and sustainable network flow model to effectively and proactively counter plausible attacking behaviors of an adversary operating under a budget constraint. Specifically, we introduce a novel scenario generation approach based on an iterative two-player game between a defender and an adversary. We assume that the adversary always takes a best myopic response (out of some feasible attacks) against the current flow scenario prepared by the defender. On the other hand, we assume that the defender considers all the attacking behaviors revealed by the adversary in previous iterations in order to generate a new conservative flow strategy that is robust (maximin) against all those attacks. This iterative game continues until the objectives of the adversary and the administrator both converge. We show that the robust network flow problem to be solved by the defender is NP-hard and that the complexity of the adversary's decision problem grows exponentially with the network size and the adversary's budget value. We propose two principled heuristic approaches for solving the adversary's problem at the scale of a large urban network. Extensive computational results on multiple synthetic and real-world data sets demonstrate that the solution provided by the defender's problem significantly increases the amount of flow pushed through the network and reduces the expected lost flow over four state-of-the-art benchmark approaches.