Kill Chain Search and Evaluation of Weapon System of Systems Based on GAT-DFS

To address the insufficient utilization of network model features and low search efficiency in kill chain analysis for Weapon System of Systems (WSoS), a complex network model of WSoS based on OODA loop was constructed, which converts the indicator system into attribute features embedded in network nodes, and analyzes the kill chain mode through the metapath. Subsequently, a Depth First Search (DFS) algorithm combined with Graph Attention Network (GAT) is proposed for kill chain search evaluation. The algorithm utilizes GAT to extract topological information and node attribute features from graph data to obtain node-embedding vectors, and optimizes the DFS algorithm process by computing the cosine similarity of node-embedding vectors. Simulation results demonstrated that the proposed algorithm achieves high search efficiency and accuracy, providing robust support for combat decision-making.

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Publication Details

Journal
Systems
Published
2025-08-16
DOI
https://doi.org/10.3390/systems13080703
Primary Topic
Military Defense Systems Analysis
Type
article
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article

Kill Chain Search and Evaluation of Weapon System of Systems Based on GAT-DFS

Huafeng He, Yongquan You, Xin Zhang, Xiang Liu et al.
Systems
Military Defense Systems Analysis
article

Kill Chain Search and Evaluation of Weapon System of Systems Based on GAT-DFS

Huafeng He, Yongquan You, Xin Zhang, Xiang Liu, Qi Zhang
article en

Abstract

To address the insufficient utilization of network model features and low search efficiency in kill chain analysis for Weapon System of Systems (WSoS), a complex network model of WSoS based on OODA loop was constructed, which converts the indicator system into attribute features embedded in network nodes, and analyzes the kill chain mode through the metapath. Subsequently, a Depth First Search (DFS) algorithm combined with Graph Attention Network (GAT) is proposed for kill chain search evaluation. The algorithm utilizes GAT to extract topological information and node attribute features from graph data to obtain node-embedding vectors, and optimizes the DFS algorithm process by computing the cosine similarity of node-embedding vectors. Simulation results demonstrated that the proposed algorithm achieves high search efficiency and accuracy, providing robust support for combat decision-making.

SystemsVol. 13(8)
PLA Rocket Force University of Engineering (CN)
Openalex Percentile: Top 71%
Military Defense Systems Analysis
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