Neural Network Approach to Ensuring the Resilience of Industrial IoT Systems under Conditions of Peripheral Node Integrity Violation

Abstract

The paper addresses the problem of ensuring the functional resilience of industrial IoT systems under conditions of partial integrity violation of peripheral nodes. The relevance of the study is driven by the growing cybersecurity risks in multi-layer edge-fog-cloud architectures, where peripheral devices perform primary processing and transmission of telemetry data. Degradation or compromise of individual nodes in such systems leads to the accumulation of aggregation errors, distortion of analytical results, and the potential adoption of incorrect management decisions. Traditional approaches based on the complete disconnection of suspicious nodes may negatively affect the continuity of technological processes and reduce the overall fault tolerance of the system. A neural network-based approach to maintaining functional resilience is proposed, combining real-time node state assessment with adaptive adjustment of their weighted contribution to processing results without complete exclusion from the computational loop. A formal model describing the impact of disturbances on the aggregated signal is developed. Within this model, a node state variable is introduced as an integral indicator of information reliability and the potential level of compromise. The assessment procedure is implemented using a gated recurrent unit (GRU) neural network, which accounts for the temporal dynamics of node behavior and enables the detection of anomalous deviations in telemetry streams. Simulation modeling demonstrated a reduction in system-level aggregation error and improved stabilization of the resulting signal compared to conventional threshold-based response methods. The obtained results confirm the effectiveness of the proposed approach for industrial IoT environments and its ability to ensure a balance between cybersecurity requirements, data reliability, and the continuity of technological processes.