Resilience Augmentation in Unmanned Weapon Systems via Multi-Layer Attention Graph Convolutional Neural Networks

The collective Unmanned Weapon System-of-Systems (UWSOS) network represents a fundamental element in modern warfare, characterized by a diverse array of unmanned combat platforms interconnected through heterogeneous network architectures. Despite its strategic importance, the UWSOS network is highly susceptible to hostile infiltrations, which significantly impede its battlefield recovery capabilities. Existing methods to enhance network resilience predominantly focus on basic graph relationships, neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS. To address these limitations, we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network (E-MAGCN), designed to augment the adaptability of UWSOS. Our approach employs BERT for extracting semantic insights from nodes and edges, thereby refining feature representations by leveraging various node and edge categories. Additionally, E-MAGCN integrates a regularization-based multi-layer attention mechanism and a semantic node fusion algorithm within the Graph Convolutional Network (GCN) framework. Through extensive simulation experiments, our model demonstrates an enhancement in resilience performance ranging from 1.2% to 7% over existing algorithms.