In scenarios such as disaster response, remote area surveillance, and military reconnaissance, where stable communication infrastructure may be unavailable, unmanned aerial vehicles (UAVs) are often deployed to physically carry data. Ensuring data reliability in such scenarios, especially in adversarial environments, requires data backup mechanisms for UAV swarms. This paper addresses the critical yet often overlooked challenge of data recovery difficulty in UAV swarm networks. We introduce Data Recovery Entropy (DRE) to quantify the spatial dispersal of backed-up data across the swarm. Based on this metric, we propose two data backup strategies: one enables adjustable level of DRE through a tunable parameter, and the other prioritizes minimizing DRE. The proposed data backup strategies are tailored to the unique characteristics of UAV swarms, focusing particularly on their dynamic communication topologies. Finally, we vary system parameters, including node deployment parameters, communication parameters, and mobility parameters, to validate the proposed approach.

This paper addresses challenges such as semantic gaps, logical inconsistencies, and neglect of domain knowledge in task decomposition for complex systems, especially in military command scenarios. We propose the BT-D2 framework — a behavior tree-guided method that integrates large language models (LLMs) with structured domain knowledge to enable dynamic and executable task decomposition. By embedding military doctrines and equipment parameters into hierarchical behavior tree nodes, the framework creates a constraint-based template for transforming high-level instructions into tactical action sequences. Key components include an extended behavior tree architecture for dynamic parameter binding, a multi-agent collaborative workflow for strategic-to-tactical planning, and an adaptive mechanism for semantic alignment and conflict resolution. Experimental results demonstrate that BT-D2 outperforms baseline methods (CoT, ReAct, GTN) in semantic integrity, decomposition diversity, and structural rationality across 200 combat scenarios. Ablation studies validate the critical role of behavior trees in enhancing task executability and hierarchical architecture in improving planning efficiency. This work contributes a novel framework for bridging generative AI with domain-specific constraints, offering promising applications in military planning, logistics, and intelligent systems.

Stealth malware, a representative tool of advanced persistent threat (APT) attacks, in particular poses an increased threat to cyber-physical systems (CPS). Due to the use of stealthy and evasive techniques (e.g., zero-day exploits, obfuscation techniques), stealth malwares usually render conventional heavyweight countermeasures (e.g., exploits patching, specialized ant-malware program) inapplicable. Light-weight countermeasures (e.g., containment techniques), on the other hand, can help retard the spread of stealth malwares, but the ensuing side effects might violate the primary safety requirement of CPS. Hence, defenders need to find a balance between the gain and loss of deploying light-weight countermeasures. To address this challenge, we model the persistent anti-malware process as a shortest-path tree interdiction (SPTI) Stackelberg game, and safety requirements of CPS are introduced as constraints in the defender's decision model. Specifically, we first propose a static game (SSPTI), and then extend it to a multi-stage dynamic game (DSPTI) to meet the need of real-time decision making. Both games are modelled as bi-level integer programs, and proved to be NP-hard. We then develop a Benders decomposition algorithm to achieve the Stackelberg Equilibrium of SSPTI. Finally, we design a model predictive control strategy to solve DSPTI approximately by sequentially solving an approximation of SSPTI. The extensive simulation results demonstrate that the proposed dynamic defense strategy can achieve a balance between fail-secure ability and fail-safe ability while retarding the stealth malware propagation in CPS.

Networks portray interactions about people communication, goods transportation and information exchange within a society, etc. How to effectively quantify the node importance is a fundamental problem in complex networks research community. In this paper, we propose a node importance evaluation method called Error Reconstruction (ER), which is mainly based on the measurement and reconstruction of errors (strong correlation with difference) between nodes in the network. First, we introduced the Network Representation Learning (NRL) approach to obtain the feature matrix about the node information and network structure, and thus we extracted the background nodes (less important nodes) to reconstruct the dense and sparse error in a single-scale network. Then we calculated the reconstruction error in a series of transformed multi-scale networks by analyzing the error with different network scales and integrated the reconstruction error with Bayesian estimation to get the final quantization of all nodes in the network. Experimental results show that the proposed method could effectively discover the vital nodes in the network.