Federated learning (FL) has become a prominent paradigm for collaborative model training while ensuring data privacy. However, in resource-constrained environments, such as the Internet of Things (IoT), FL faces a distinct challenge from Lazybone attackers, who compromise system performance by providing low-quality data or conducting minimal local training to reduce their computational burden. In this article, we propose Fedeval, a novel multi-dimensional evaluation framework designed to defend against Lazybone attacks. Fedeval leverages a server-side base validation dataset and a base model to assess the quality and relevance of client contributions through gradient inversion, and it compares client-uploaded gradients with an honest baseline to detect training inconsistencies. By assigning adaptive importance scores based on client contributions, Fedeval enhances the robustness of FL by mitigating the impact of non-contributing participants. We also provide a theoretical analysis of Fedeval’s convergence properties and validate its effectiveness through extensive experiments on four datasets and two attack scenarios. Our results demonstrate that Fedeval significantly accelerates convergence and improves accuracy by up to 13% compared to traditional methods.

Online social networks provide convenient conditions for the spread of rumors, and false rumors bring great harm to social life. Rumor dissemination is a process, and effective identification of rumors in the early stage of their appearance will reduce the negative impact of false rumors. This paper proposes a novel early rumor detection (ERD) model based on reinforcement learning. In the rumor detection part, a dual-engine rumor detection model based on deep learning is proposed to realize the differential feature extraction of original tweets and their replies. A double self-attention (DSA) mechanism is proposed, which can eliminate data redundancy in sentences and words at the same time. In the reinforcement learning part, an ERD model based on Deep Recurrent Q-Learning Network (DRQN) is proposed, which uses LSTM to learn the state sequence features, and the optimization strategy of the reward function is to take into account the timeliness and accuracy of rumor detection. Experiments show that, compared with existing methods, the ERD model proposed in this paper has a greater improvement in the timeliness and detection rate of rumor detection.

The rapid development in network technology has resulted in the proliferation of Internet of Things (IoT). This trend has led to a widespread utilization of decentralized data and distributed computing power. While machine learning can benefit from the massive amount of IoT data, privacy concerns and communication costs have caused data silos. Although the adoption of blockchain and federated learning technologies addresses the security issues related to collusion attacks and privacy leakage in data sharing, the “free-rider attacks” and “model poisoning attacks” in the federated learning process require auditing of the training models one by one. However, that increases the communication cost of the entire training process. Hence, to address the problem of increased communication cost due to node security verification in the blockchain-based federated learning process, we propose a communication cost optimization method based on security evaluation. By studying the verification mechanism for useless or malicious nodes, we also introduce a double-layer aggregation model into the federated learning process by combining the competing voting verification methods and aggregation algorithms. The experimental comparisons verify that the proposed model effectively reduces the communication cost of the node security verification in the blockchain-based federated learning process.

In this issue, we consider the location-aware targeted influence blocking maximization (LTIBM) problem, which plays a very important role in viral marketing and rumor control. LTIBM aims to find a set of positive seeds in a given social network to block the influence propagation of negative seeds over the targeted nodes located in a given region and having a preference on a given topic set as much as possible. We devise a simulation-based greedy algorithm based on monotone and submodular characteristics of influence function under the homogeneous independent cascade model. To improve the efficiency of the greedy algorithm, we propose LTIBM-H, a heuristic algorithm based on QT-tree and maximum influence arborescence (MIA). Experimental results show that the proposed LTIBM-H algorithm can achieve matching the blocking effect to the greedy algorithm and often performs better in terms of effectiveness than other baseline algorithms, while LTIBM-H is four orders of magnitude faster than the greedy algorithm.

While mobile networks provide many opportunities for people, they face security problems huge enough that a firewall is essential. The firewall in mobile networks offers a secure intranet through which all traffic is handled and processed. Furthermore, due to the limited resources in mobile networks, the firewall execution can impact the quality of communication between the intranet and the Internet. In this paper, a performance evaluation mathematical model for firewall system of mobile networks is developed using queuing theory for a multihierarchy firewall with multiple concurrent services. In addition, the throughput and the package loss rate are employed as performance evaluation indicators, and discrete‐event simulated experiments are conducted for further verification. Lastly, experimental results are compared to theoretically obtained values to identify a resource allocation scheme that provides optimal firewall performance and can offer a better quality of service (QoS) in mobile networks.

Web Security Gateway (WSG) is a new type of network security product that maintains the security of trusted networks. In this paper, a WSG model for evaluating WSG performance is presented. This paper advances discussion of previous studies on series services under multiple service windows. The proposed model consists of a two-stage queuing system. The first stage is a network layer simulation. The second stage is thus similar to a parallel hyper-Erlang distribution model. The results of a simulation test verified the feasibility and performance of the proposed model.

Occupancy information is one of the most important privacy issues of a home. Unfortunately, an attacker is able to detect occupancy from smart meter data. The current battery-based load hiding (BLH) methods cannot solve this problem. To thwart occupancy detection attacks, we propose a framework of battery-based schemes to prevent occupancy detection (BPOD). BPOD monitors the power consumption of a home and detects the occupancy in real time. According to the detection result, BPOD modifies those statistical metrics of power consumption, which highly correlate with the occupancy by charging or discharging a battery, creating a delusion that the home is always occupied. We evaluate BPOD in a simulation using several real-world smart meter datasets. Our experiment results show that BPOD effectively prevents the threshold-based and classifier-based occupancy detection attacks. Furthermore, BPOD is also able to prevent nonintrusive appliance load monitoring attacks (NILM) as a side-effect of thwarting detection attacks.