  This paper studies a defense approach against one or more swarms of
adversarial agents. In our earlier work, we employ a closed formation
(`StringNet') of defending agents (defenders) around a swarm of adversarial
agents (attackers) to confine their motion within given bounds, and guide them
to a safe area. The control design relies on the assumption that the
adversarial agents remain close enough to each other, i.e., within a prescribed
connectivity region. To handle situations when the attackers no longer stay
within such a connectivity region, but rather split into smaller swarms
(clusters) to maximize the chance or impact of attack, this paper proposes an
approach to learn the attacking sub-swarms and reassign defenders towards the
attackers. We use a `Density-based Spatial Clustering of Application with Noise
(DBSCAN)' algorithm to identify the spatially distributed swarms of the
attackers. Then, the defenders are assigned to each identified swarm of
attackers by solving a constrained generalized assignment problem. Simulations
are provided to demonstrate the effectiveness of the approach.
