  Intrusion Response is a relatively new field of research. Recent approaches
for the creation of Intrusion Response Systems (IRSs) use Reinforcement
Learning (RL) as a primary technique for the optimal or near-optimal selection
of the proper countermeasure to take in order to stop or mitigate an ongoing
attack. However, most of them do not consider the fact that systems can change
over time or, in other words, that systems exhibit a non-stationary behavior.
Furthermore, stateful approaches, such as those based on RL, suffer the curse
of dimensionality, due to a state space growing exponentially with the size of
the protected system.
  In this paper, we introduce and develop an IRS software prototype, named
irs-partition. It leverages the partitioning of the protected system and Deep
Q-Networks to address the curse of dimensionality by supporting a multi-agent
formulation. Furthermore, it exploits transfer learning to follow the evolution
of non-stationary systems.
