The Internet of Vehicles (IoV) is a network system that enables wireless communication and information exchange between vehicles and other traffic participants. Intrusion detection plays a very important role in the IoV. However, with the development of the IoV, unknown attack behaviors may appear. The lack of analysis and collection of these attack behavior has led to an imbalance in the sample data categories of the IoV intrusion detection, which causes the problem of low detection accuracy. At the same time, the intrusion detection model usually needs to upload data to the cloud for training, which will introduce the privacy risk due to of the leakage of vehicle users’ information. In this paper, we propose an intrusion detection method for the IoV based on federated learning and memory-augmented autoencoder (FL-MAAE). We add a memory module to the autoencoder model to enhance its ability to store the behavior feature patterns of the IoV, make it robust to imbalanced samples, and use the reconstruction error as the evaluation index, so as to detect unknown attacks in the IoV. We propose a federated learning based training method for the IoV intrusion detection model. Local training of intrusion detection models in roadside units can effectively protect the privacy of data resources. We also designed an aggregation method based on the performance contribution of participants to improve the reliability of model aggregation. We conducted experiments on the NSL-KDD intrusion detection dataset to evaluate the performance of the proposed method. Experimental results show that our method has the best intrusion detection performance. In the case of contaminated samples, the accuracy and F1 score of the proposed method are 9.6% and 7.39% higher than those of the comparison methods on average.

The onset of Web 3.0 has catalyzed the rapid advancement of social networking, transforming platforms into essential elements deeply embedded within the fabric of daily life. Researchers have proposed several methods for detecting anomalous behaviors in various scenarios. This article provides a comprehensive review of current research and the latest developments in anomalous behavior detection within social networks. We present a hierarchical three-layer categorization scheme based on the distinct characteristics of base-level detection technologies and various datasets. First, anomaly detection based on user behavioral characteristics can intuitively reflect deviations in individual behavior. However, it may overlook the overall network structure’s impact. Second, detecting anomalies within a network’s topological structure highlights structural significance, but may overlook the subtle nuances of individual behavior. Finally, the coordinated fusion method, which blends individual behavioral characteristics and the network’s topological structure, addresses the multifaceted nature of anomalies, yielding a more thorough and accurate anomaly detection strategy. This paper provides an overview and assesses the performance of three anomaly detection methods. Furthermore, we explore the challenges associated with social network anomaly detection and the potential pathways for further research.

In massive MIMO systems, the high complexity of signal detection comes mainly from computing a Gram matrix and its inversion. In this correspondence, we propose a low-complexity MIMO detection method based on the Neumann series (NS), which does not require explicitly computing the Gram matrix and its inversion. The proposed method leverages the statistical information of the Gram matrix to compute an initialization matrix, which guarantees the convergence of NS expansion. The signal detection is conducted implicitly by transforming matrix inversion and product operations into matrix-vector multiplication operations. By doing so, it reduces the computational complexity of MIMO signal detection from $\mathcal {O}(BU^{2}+U^{3})$ to $\mathcal {O}(BUN)$ , where $B$ , $U$ , and $N$ are the numbers of antennas at the base station, at the user equipment, and NS terms, respectively. Simulation results show that, compared with existing approaches, the proposed method can significantly reduce the signal detection complexity while achieving similar performance.

With the rapid advancement of in-vehicle communication technology, vehicular edge caching has garnered considerable attention as a pivotal technology to improve the efficiency of data transmission. However, existing studies often overlook the issues of increased average content access latency and decreased caching hit rate, stemming from the conflict between limited storage space in in-vehicle edge servers and vehicle mobility. To address these issues, this paper proposes a Multi-agent Federated Deep Reinforcement Learning based Collaborative Caching Strategy (MFDRL-CCS), leveraging Vehicle-to-Vehicle (V2V) communications. Specifically, we first perform vehicle connectivity prediction based on Recurrent Neural Network (RNN) considering the characteristics of vehicle nodes and their interrelations. Then, the optimal caching vehicle is selected based on the connectivity between vehicle nodes and the density of vehicle nodes. Meanwhile, a Multi-Head Attention Popularity Prediction (MHAPP) model is also constructed, which amalgamates multi-dimensional features, including historical popularity, social relationships, and geographic location, to predict content popularity. Finally, the edge collaborative caching model is formulated as a Markov Decision Process (MDP). Under the multi-agent competitive deep Q-learning framework, each vehicle learns the optimal caching strategy through an independent Q-network to maximize long-term rewards, and uses federated learning to train the caching replacement algorithm in a distributed manner. Compared to existing caching policies, the caching policy proposed in this paper improves the caching hit rate by approximately 19.8% and reduces the content access latency by about 12.5%.

The problems with network security that the Internet of Vehicles (IoV) faces are becoming more noticeable as it continues to evolve. Deep learning-based intrusion detection techniques can assist the IoV in preventing network threats. However, previous methods usually employ a single deep learning model to extract temporal or spatial features, or extract spatial features first and then temporal features in a serial manner. These methods usually have the problem of insufficient extraction of spatio-temporal features of the IoV, which affects the performance of intrusion detection and leads to a high false-positive rate. To solve the above problems, this paper proposes an intrusion detection method for IoV based on parallel analysis of spatio-temporal features (PA-STF). First, we built an optimal subset of features based on feature correlations of IoV traffic. Then, we used the temporal convolutional network (TCN) and long short-term memory (LSTM) to extract spatio-temporal features in the IoV traffic in a parallel manner. Finally, we fused the spatio-temporal features extracted in parallel based on the self-attention mechanism and used a multilayer perceptron to detect attacks in the Internet of Vehicles. The experimental results show that the PA-STF method reduces the false-positive rate by 1.95% and 1.57% on the NSL-KDD and UNSW-NB15 datasets, respectively, with the accuracy and F1 score also being superior.

This article investigates the performance-guaranteed adaptive fuzzy wavelet neural fixed-time control design with singularity-avoidance for the unmanned surface vehicle (USV) under switching event-triggered communication. First, by resorting to the convergence of the fixed-time performance function, the tracking error is driven into the anticipated steady-state interval with the specified evolving behavior. Moreover, the unknown dynamics of the controlled vehicle are modeled approximately by the fuzzy wavelet neural networks, while a compensation function is designed to overcome the composite perturbation comprising the external disturbances and estimation errors. In addition, a switching event-triggered mechanism-based adaptive fixed-time control design is proposed, which not only achieves non-periodic updating of the control signal but also effectively eliminates control singularity and computational complexity present in traditional recursive control frameworks. Stability analysis confirms the practical fixed-time stability of the closed-loop system. Finally, illustrative results are provided to validate the effectiveness and feasibility of the developed scheme.