The rise of programmable data plane (PDP) and in-network computing (INC) paradigms paves the way for the development of network devices (switches, network interface cards, etc.) capable of performing advanced computing tasks. This allows to execute algorithms of various nature, including machine learning ones, within the network itself to support user and network services. In particular, this paper delves into the issue of implementing in-network learning models to support distributed intrusion detection systems (IDS). It proposes a model that optimally distributes the IDS workload, resulting from the subdivision of a "Strong Learner" (SL) model into lighter distributed "Weak Learner" (WL) models, among data plane devices; the objective is to ensure complete network security without excessively burdening their normal operations. Furthermore, a meta-heuristic approach is proposed to reduce the long computational time required by the exact solution provided by the mathematical model, and its performance is evaluated. The analysis conducted and the results obtained demonstrate the enormous potential of the proposed new approach to the creation of intelligent data planes that effectively act as a first line of defense against cyber attacks, with minimal additional workload on network devices.

Defending computer networks from cyber attack requires timely responses to alerts and threat intelligence. Decisions about how to respond involve coordinating actions across multiple nodes based on imperfect indicators of compromise while minimizing disruptions to network operations. Currently, playbooks are used to automate portions of a response process, but often leave complex decision-making to a human analyst. In this work, we present a deep reinforcement learning approach to autonomous response and recovery in large industrial control networks. We propose an attention-based neural architecture that is flexible to the size of the network under protection. To train and evaluate the autonomous defender agent, we present an industrial control network simulation environment suitable for reinforcement learning. Experiments show that the learned agent can effectively mitigate advanced attacks that progress with few observable signals over several months before execution. The proposed deep reinforcement learning approach outperforms a fully automated playbook method in simulation, taking less disruptive actions while also defending more nodes on the network. The learned policy is also more robust to changes in attacker behavior than playbook approaches.

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