Adaptive Intrusion Response via Federated Meta-Learning for IIoT Zero-Day Mitigation

Abstract

The industrial evolution has meant that Industrial Internet of Things (IIoT) devices have exponentially increased, and as such, industrial automation has now become a reality since they are capable of monitoring in real-time, making predictions, and improving efficiency. These environments are fluid and privacy-sensitive environments that need collaborative and privacy-preserving learning models that are able to rapidly adapt to the emerging threats. Federated meta-learning has become a potentially promising method that unites the flexibility of meta-learning and the distributed and privacy-concerned design of federated learning. This document suggests an adaptive security solution to employ Model-Agnostic Meta-Learning (MAML) and Reptile-based federated approaches to intrusion response and zero-day attacks prevention of IIoT systems. The experimental data needed to train and test involves synthetic traffic of IIoT networks that is simulated using stochastic attack generator modules. Indicators of performance, namely detection performance, false positive rate, latency, and convergence efficiency, were provided by means of classification tools in the form of confusion matrix visualization, ROC curves, and loss progression graphs. The framework has been tested and run in a simulated environment of controlled tests in which MATLAB code driven by a matrix has internal data and inbuilt comparative agents. The experiments reveal that MAML-FL has a better result when it comes to generalization and zero-day threats mitigation, whereas Reptile-FL is more efficient in terms of seeking communication and faster convergence rates. In this paper, the authors present a scalable and robust architecture capable of providing a trade-off between real-time learning, adversarial robustness, and communication efficiency, thereby making the IIoT ecosystems intelligent and secure in their automation.