Optimizing service performance in UAV-assisted Mobile Edge Computing (MEC) systems often relies on effective cooperative trajectory and resource allocation. Federated Reinforcement Learning (FRL) provides a distributed paradigm to collaboratively learn these policies without sharing raw observations. However, practical deployments face severe coupled challenges: the model quality degradation caused by Differential Privacy (DP) noise injection, heterogeneous environments (Non-IID data), and complex physical constraints. To mitigate these issues, we propose PQ-FRL, a Privacy-Preserving and Quality-Aware FRL framework. Our approach utilizes an Output Perturbation DP mechanism to provide strict per-round protection for uploaded policy updates, representing a pragmatic engineering trade-off between securing immediate operational privacy and maintaining continuous-control DRL convergence. To address the mixed-action space, we incorporate a Straight-Through Estimator (STE) for differentiable offloading decisions, guided by a conditionally shaped reward to prevent suboptimal local equilibria. Furthermore, a novel Quality-Aware (QA) aggregation mechanism dynamically assigns weights based on explicitly DP-protected local reward signals, helping to disentangle noise from inherently poor performance. Simulation results indicate that PQ-FRL can effectively balance realistic non-linear energy and latency constraints. Under the evaluated heterogeneous scenarios and strict privacy budgets, our method demonstrates robust utility preservation and exhibits graceful degradation as physical airspace congestion increases with larger swarm sizes, offering a stable and practical solution for privacy-sensitive UAV edge computing.

The advent of unmanned aerial vehicle (UAV) swarm technology brings possibilities to help humans complete tasks in no man’s land, such as deserts and rainforests. However, UAV network faces many cyber threats, where attackers can impersonate legitimate entities or tamper with UAV task data. For identity security, most of the existing methods use centralized authentication schemes, which have a single point of failure problem. For data security, the existing methods only secure the task data in the ground system, ignoring the data security in the air network. Therefore, the existing methods are not suitable for ubiquitous UAV scenarios. Blockchain secures data security while eliminating the single point of failure problem, and has been widely used in distributed scenarios. In this article, to secure entity identity and task data, we propose a blockchain-supported secure multi-UAV task management scheme (B-UAVM). Specifically, a three-layer blockchain structure is constructed to secure multitasks, and achieve ubiquitous control of UAV formations. Besides, six types of blocks and three types of transactions are designed to achieve safe processing and storage of task data and entity information. Furthermore, an improved practical byzantine fault tolerance (IPBFT) consensus mechanism and a UAV-formation-action-considered ground station consensus mechanism (UFAGS) are introduced in the Server Network and Ground Control Network, respectively, to accelerate the consensus. The experimental results show that the number of transactions generated per second (TPS) of B-UAVM is about$0.5\times $and$3.7\times $of the existing method when the block size or the number of blockchain nodes increases, respectively.

To build a secure communications software, Vulnerability Prediction Models (VPMs) are used to predict vulnerable software modules in the software system before software security testing. At present many software security metrics have been proposed to design a VPM. In this paper, we predict vulnerable classes in a software system by establishing the system's weighted software network. The metrics are obtained from the nodes' attributes in the weighted software network. We design and implement a crawler tool to collect all public security vulnerabilities in Mozilla Firefox. Based on these data, the prediction model is trained and tested. The results show that the VPM based on weighted software network has a good performance in accuracy, precision, and recall. Compared to other studies, it shows that the performance of prediction has been improved greatly in Pr and Re.

In recent years, network worm that had a dramatic increase in the frequency and virulence of such outbreaks have become one of the major threats to the security of the Internet. To develop appropriate tools for thwarting quick spread of worms, researchers are trying to understand the behavior of the worm propagation with the aid of epidemiological models. In this paper, a new worm propagation model with bilinear incidence rate is proposed and discussed based on the classical Kermack-Mckendrick (KM) model. It supports the increase rate of new hosts and the decrease rate of hosts leaving from the network. Moreover, global stability of equilibriums of the model is analyzed in detail; the numerical simulations confirmed well the theoretical results. So our study can provide insight into taking effective measures to prevent and control the spread of worm in the network.