Stochastic optimization of flow-jamming attacks in multichannel wireless networks

An attacker can launch an efficient jamming attack to deny service to flows in wireless networks by using cross-layer knowledge of the target network. For example, flow-jamming defined in existing work incorporates network layer information into the conventional jamming attack to maximize its attack efficiency. In this paper, we redefine a discrete optimization model of flow-jamming in multichannel wireless networks and provide metrics to evaluate the attack efficiency. We then propose the use of stochastic optimization techniques for flow-jamming attacks by using three stochastic search algorithms: iterative improvement, simulated annealing, and genetic algorithm. By integrating the algorithms into a simulation based on the OPNET Modeler network simulator, we demonstrate the optimization process and provide performance comparisons of the algorithms. From our results, genetic algorithm provides the most efficient flow-jamming configuration.