The industrial internet of things (IIoT) has been widely applied in fields such as smart manufacturing, energy management, and infrastructure monitoring. Its distributed nature and cross-domain data collaboration requirements make federated learning (FL) an important modeling paradigm. However, FL systems in IIoT are vulnerable to backdoor attacks, where adversaries implant hidden trigger patterns during local model training to induce the global model to produce malicious outputs for specific inputs. To address this issue, we propose a federated backdoor defense method named FedQmc, which is based on quantum state encoding and multi-scale aggregation. Specifically, unlike traditional detection methods that rely on geometric distances in parameter space, we design a quantum state encoding-driven anomaly measurement mechanism. By encoding local updates into quantum states, model updates can be represented in high-dimensional complex Hilbert space, and the coherence and superposition properties of quantum states are exploited to enhance feature discrimination, thereby providing significant advantages in capturing subtle yet highly correlated backdoor perturbations. Based on this, we propose multi-scale robust aggregation, which simultaneously suppresses different types of backdoor updates at three levels: layer scale, block scale, and low-rank subspace scale. In addition, we develop a meta-learning-based adaptive weight optimization mechanism, which enables the aggregation weights at each scale to be dynamically adjusted across multiple training rounds. This allows FedQmc to resist stronger and stealthier backdoor attacks while maximally retaining useful gradient information from benign clients. Theoretical analysis demonstrates the convergence and security guarantees of FedQmc. Experimental results show that FedQmc significantly reduces the attack success rate while maintaining high accuracy across multiple datasets, thereby effectively enhancing multi-scale defense capability in IIoT scenarios.

In multi-access edge computing (MEC), intelligent services are pre-deployed across various edge servers to enable users to offload their tasks and enhance their experience. While dual-timescale frameworks have been proposed to balance long-term service stability and short-term task flexibility, existing approaches largely overlook the intricate dependencies among services and tasks, leading to poor scalability and suboptimal performance in real-world dynamic environments. To bridge this gap, we introduce a spatiotemporal-aware (ST-aware) dual timescale solution for joint service deployment and dependent task offloading in MEC. We first model the joint problem as an integer nonlinear programming (INLP) problem and analyze its NP-hardness. We then reformulate it as a partially observable markov decision process (POMDP) and propose DualFlare, a novel decentralized framework that integrates a greedy global priority task offloading strategy at small timescale and an ST-collaboration service deployment mechanism at large timescale. Our approach leverages a centralized training with decentralized execution (CTDE) paradigm enhanced by knowledge distillation, enabling edge servers to learn globally coordinated yet locally adaptive deployment policies. Extensive experiments demonstrate that DualFlare outperforms state-of-the-art baselines by a significant margin, reducing average user latency by 53.23%, energy consumption by 68.77%, and violation rate by 40.70%, while also achieving remarkable scalability in large-scale MEC scenarios.

With the flourishing of the credit card business and Internet technology, the risk of fraudulent credit card transactions is ever-increasing due to the complex information involved in the credit card business. Since the high redundancy of feature information and imbalance of class distribution in transaction data, the performance of the existing machine learning-based models for detecting credit card fraudulent transactions still needs to be improved. Therefore, it is crucial to build fraud detection models for effective feature engineering and sampling techniques. This article proposes a credit card fraud detection model incorporating a fraud feature-boosting mechanism with a spiral oversampling balancing technique (SOBT). Specifically, we present a compound grouping elimination strategy to exclude highly redundant and correlated features from the credit card transaction dataset and improve the data quality. Furthermore, we design a multifactor synchronous embedding mechanism, which combines the performance evaluation metrics of the embedding model for each feature and improves the decision-making ability of each feature for the target domain. Moreover, we propose an SOBT to balance the ratio of legitimate to fraudulent transactions, which improves the ability of the fraud detection model to distinguish legitimate from fraudulent transactions. Extensive experimental results based on two real-world datasets demonstrate that our methods can facilitate efficient credit card fraud detection and achieve better performance than state-of-the-art methods.

For enhancing the utilization of radio frequency (RF) bands, dynamic spectrum access has been considered to be an emerging paradigm where unlicensed secondary users access the licensed RF spectrum opportunistically without causing harmful interference to licensed primary users. In order to access idle channels, secondary users are required either to sense channels or search the geolocation based spectrum database before starting their actual communications. Note that the database searching approach has been proven to be more effective than the channel sensing approach since the sensing based approach results in higher sensing uncertainties. When a large number of secondary users query the spectrum database for finding idle channels at the same time, the spectrum server could be overwhelmed that could result in (unintentional) Denial-of-Service attack. In this paper, we investigate group-query-as- aservice for searching idle channels in database driven dynamic spectrum access where selected secondary users (aka grid leaders who are selected based on the interactive trust levels, location and resources in their grids) query on behalf of the other secondary users (aka grid followers). In this approach, secondary users are associated with the contours created based on same idle channels with the help of spectrum sensors and mobile edge computing (MEC) servers at each base station. The performance of the proposed group-query-as-a-service is evaluated using numerical results obtained from Monte Carlo simulation. We found that the proposed approach results in less delay with increase edge hit rate and higher throughput than the individual query based approach.

Applying the proliferated location-based services (LBSs) to social networks has spawned mobile social network (MSN) services that allow users to discover potential friends around them. While enjoying the convenience of MSN services, the mobile users also are confronted with the risk of location disclosure, which is a severe privacy preserving concern. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a repartitioning anonymous region for location privacy preserving (RPAR) scheme based on the central anonymous location which minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location. Furthermore, our scheme enables the users to get more accurate query results, thus improving the quality of the location service. Simulation results show that our proposed scheme can effectively reduce the area of anonymous regions and minimize the traffic.

Fog computing, also called “clouds at the edge,” is an emerging paradigm allocating services near the devices to improve the quality of service (QoS). The explosive prevalence of Internet of Things, big data, and fog computing in the context of cloud computing makes it extremely challenging to explore both cloud and fog resource scheduling strategy so as to improve the efficiency of resources utilization, satisfy the users' QoS requirements, and maximize the profit of both resource providers and users. This paper proposes a resource allocation strategy for fog computing based on priced timed Petri nets (PTPNs), by which the user can choose the satisfying resources autonomously from a group of preallocated resources. Our strategy comprehensively considers the price cost and time cost to complete a task, as well as the credibility evaluation of both users and fog resources. We construct the PTPN models of tasks in fog computing in accordance with the features of fog resources. Algorithm that predicts task completion time is presented. Method of computing the credibility evaluation of fog resource is also proposed. In particular, we give the dynamic allocation algorithm of fog resources. Simulation results demonstrate that our proposed algorithms can achieve a higher efficiency than static allocation strategies in terms of task completion time and price.