Blind Network Interdiction Strategies—A Learning Approach

Network interdiction refers to disrupting a network in an attempt to either analyze the network’s vulnerabilities or to undermine a network’s communication capabilities. A vast majority of the works that have studied network interdiction assume a priori knowledge of the network topology. However, such knowledge may not be available in real-time settings. For instance, in practical electronic warfare-type settings, an attacker that intends to disrupt communication in the network may not know the topology a priori. Hence, it is necessary to develop online learning strategies that enable the attacker to interdict communication in the underlying network in real-time. In this paper, we develop several learning techniques that enable the attacker to learn the best network interdiction strategies (in terms of the best nodes to attack to maximally disrupt communication in the network) and also discuss the potential limitations that the attacker faces in such blind scenarios. We consider settings where 1) only one node can be attacked and 2) where multiple nodes can be attacked in the network. In addition to the single-attacker setting, we also discuss learning strategies when multiple attackers attack the network and discuss the limitations they face in real-time settings. Several different network topologies are considered in this study using which we show that under the blind settings considered in this paper, except for some simple network topologies, the attacker cannot optimally (measured in terms of the number of flows stopped) attack the network.