Kexin Wang*, Yingdong Gou, Dingrui Xue*, Jiancheng Liu, Wanlong Qi, Gang Hou, Bo Li
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2941-2962, 2024, DOI:10.32604/cmc.2024.052893
- 15 August 2024
Abstract 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 More >
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