Network Interdiction Using Adversarial Traffic Flows

Traditional network interdiction refers to the problem of an interdictor trying to reduce the throughput of network users by removing network edges. In this paper, we propose a new paradigm for network interdiction that models scenarios, such as stealth DoS attack, where the interdiction is performed through injecting adversarial traffic flows. Under this paradigm, we first study the deterministic flow interdiction problem, where the interdictor has perfect knowledge of the operation of network users. We show that the problem is highly inapproximable on general networks and is NP-hard even when the network is acyclic. We then propose an algorithm that achieves a logarithmic approximation ratio and quasi-polynomial time complexity for acyclic networks through harnessing the submodularity of the problem. Next, we investigate the robust flow interdiction problem, which adopts the robust optimization framework to capture the case where definitive knowledge of the operation of network users is not available. We design an approximation framework that integrates the aforementioned algorithm, yielding a quasi-polynomial time procedure with poly-logarithmic approximation ratio for the more challenging robust flow interdiction. Finally, we evaluate the performance of the proposed algorithms through simulations, showing that they can be efficiently implemented and yield near-optimal solutions

Modeling Network Interdiction Tasks

Mission planners seek to target nodes and/or arcs in networks that have the greatest benefit for an operational plan. In joint interdiction doctrine, a top priority is to assess and target the enemy\u27s vulnerabilities resulting in a significant effect on its forces. An interdiction task is an event that targets the nodes and/or arcs of a network resulting in its capabilities being destroyed, diverted, disrupted, or delayed. Lessons learned from studying network interdiction model outcomes help to inform attack and/or defense strategies. A suite of network interdiction models and measures is developed to assist decision makers in identifying critical nodes and/or arcs to support deliberate and rapid planning and analysis. The interdiction benefit of a node or arc is a measure of the impact an interdiction task against it has on the residual network. The research objective is achieved with a two-fold approach. The measures approach begins with a network and uses node and/or arc measures to assess the benefit of each for interdiction. Concurrently, the models approach employs optimization models to explicitly determine the nodes and/or arcs that are most important to the planned interdiction task

Efficiently interdicting a time-expanded transshipment network

A network interdictor' has a limited supply of resource with which to disrupt a network user's" flow of supplies in a capacitated transshipment network. The interdictor's problem of minimizing the maximum flow through the network is a difficult- to-solve integer programming problem but we show that a heuristic based on Lagrangian relaxation is very effective in approximately solving the problem. We implement algorithms in C to approximately solve both the static (without considering time) and dynamic network interdiction problems. Static test networks range in size from 25 nodes and 64 arcs to 400 nodes and 1519 arcs. Using an IBM Rs/6000 Model 590 workstation, we find optimal solutions for seven of 12 test networks and solve the largest problem in only 31.0 seconds. We model a dynamic network in time-expanded form in order to assign time weights of 0 or 1 to flow, include repair time of interdicted arcs, and provide a schedule to the network interdictor that identifies arcs and time periods for interdictions. Dynamic networks range in size from 525 nodes and 1, 344 arcs to 40,400 nodes and 153,419 arcs (in time-expanded form). We find near- optimal solutions in 13 of 24 test networks and solve the largest network in 1729.5 secondshttp://archive.org/details/efficientlyinter00derbLieutenant Commander, United States NavyApproved for public release; distribution is unlimited