With the rapid advancement of ubiquitous connectivity, the Internet of Unmanned Agents (IUA) has emerged as a promising paradigm for distributed intelligent perception and decision-making. In such systems, numerous unmanned agents collaboratively collect environmental data to support coordinated tasks. To preserve data privacy, Federated Learning (FL), as a decentralized machine learning framework, enables collaborative training of a global model across agents without directly exchanging raw data. However, FL faces several critical challenges in IUA environments, including high communication overhead under constrained wireless bandwidth, potential privacy leakage from shared model parameters, and agent dropouts caused by unstable connectivity. To address these challenges, we propose PPE-FL, a privacy-preserving and communication-efficient federated learning scheme designed for IUA scenarios. PPE-FL replaces conventional high-dimensional gradient uploads with lightweight ranking-based votes, substantially reducing communication overhead. Then, we design an obfuscation mechanism to protect the privacy of locally generated ranking-based votes, safeguarding both raw data and intermediate parameters. Furthermore, PPE-FL supports mask reconstruction through partial interactions among online unmanned agents, enabling robust aggregation under dynamic network conditions. Experimental results demonstrate that the proposed PPE-FL reduces communication costs by 89.5% compared to VCD-FL and by 87.7% compared to PPML, while maintaining model accuracy and privacy protection.

In interconnected industrial control networks like smart grids, topology attacks on physical grids can lead to severe cascading failures and large-scale blackouts. Effective defense on vulnerable devices can significantly reduce the risk of cascading failures and improve overall system robustness. In this paper, we investigate the vulnerability analysis problem from a graph theoretical classification perspective. By calculating a node vulnerability vector composed of features based on complex network theory, node embedding, extended betweenness and power flow distribution, we propose a node vulnerability analysis method based on XGBoost classifier. A cascading failure simulation model based on DC power flow is used to simulate the smart grid behaviours under topology attacks and create the dataset for the XGBoost classifier. The effectiveness of the proposed XGBoost-based method with newly-introduced features is demonstrated by case studies.

The pilot's error is one of the important factors affecting flight safety, which may cause serious consequences. This article mainly discusses the continuous operating deviation and sudden large-scale misoperation during the flight process. For the two types of error models, the controllers are designed to reduce the impact after the misoperation occurs, and simulation verification is performed. Simulation results show that the L1 adaptive controller can effectively suppress the impact of continuous operating deviation on control performance and improve flight comfort; the incremental nonlinear dynamic inverse controller can effectively reduce the impact of large misoperation and restore the original flight state.

Differential Fault Attack (DFA) is a powerful cryptanalytic technique to retrieve secret keys by exploiting the faulty ciphertexts generated during encryption procedure. This article proposes a novel DFA attack that is effective on ITUbee, a software-oriented block cipher for resource-constrained devices. Different from other DFA, our attack makes use of not only faulty values, but also differences between fault-free intermediate values corresponding to 2 plaintexts, which combine traditional differential analysis with DFA. The possible injection positions with different number of faults are discussed. The most efficient attack takes 2 25 round function operations with 4 faults, which is achieved in a few seconds on a PC.

Cayley data centers are well known patterns of completely wireless data centers (WDCs). However, low link reliability and link interference will affect the construction of virtual networks. This paper proposes a virtual resource mapping algorithm on the basis of Cayley structures. First, we analyze the characteristics of Cayley WDCs and model networks in WDCs, where a virtual network is modeled as a traditional undirected graph, while a physical topology is modeled as a directed graph. Second, we propose a virtual resource mapping and coloring algorithm based on link interference called VRMCA-LI. We build a connection interference matrix for each node and use a coloring method to avoid interference. VRMCA-LI uses the same color for the nodes that are within the transmitting angle of a sending node and whose signal-to-noise ratio is less than a threshold. These nodes with the same color cannot be allocated to virtual nodes at the same time. The allocation of nodes and links are concurrent, which performs dynamic adjustment to save mapping time. Third, our experimental results show that VRMCA-LI outperforms WVNEA-LR and PG-VNE in terms of mapping time of virtual nodes, acceptance rate of virtual networks, and average node utilization rate.