With the rapid development of the internet worldwide, network security issues are becoming increasingly prominent. Network intrusion detection systems (NIDSs) play a vital role in ensuring computer network security due to their ability to identify potential network threats. Despite considerable research efforts, deploying NIDSs on resource-constrained devices has been challenging. To reduce the imposed computational cost and model storage requirements, in this paper, we propose a novel lightweight NIDS model. In this model, patterns of normal and malicious actions are learned via a finite Dirichlet mixture model (DMM) in the context of the extended stochastic variational inference (ESVI) framework. With the proposed method, both the parameter estimation and model selection processes can be simultaneously addressed in a unified Bayesian framework. A great number of experiments conducted on three publicly available datasets demonstrate that the proposed model not only achieves comparable classification performance to that of detection models based on several well-studied finite mixture modeling, traditional machine learning (ML) and promising deep learning (DL) algorithms but also significantly reduces the required training and detection time. Extensive experimental results validate that the proposed model is a feasible and efficient lightweight intrusion detection model.

The finite inverted beta mixture model (IBMM) has been proven to be efficient in modeling positive vectors. Under the traditional variational inference framework, the critical challenge in Bayesian estimation of the IBMM is that the computational cost of performing inference with large datasets is prohibitively expensive, which often limits the use of Bayesian approaches to small datasets. An efficient alternative provided by the recently proposed stochastic variational inference (SVI) framework allows for efficient inference on large datasets. Nevertheless, when using the SVI framework to address the non-Gaussian statistical models, the evidence lower bound (ELBO) cannot be explicitly calculated due to the intractable moment computation. Therefore, the algorithm under the SVI framework cannot directly use stochastic optimization to optimize the ELBO, and an analytically tractable solution cannot be derived. To address this problem, we propose an extended version of the SVI framework with more flexibility, namely, the extended SVI (ESVI) framework. This framework can be used in many non-Gaussian statistical models. First, some approximation strategies are applied to further lower the ELBO to avoid intractable moment calculations. Then, stochastic optimization with noisy natural gradients is used to optimize the lower bound. The excellent performance and effectiveness of the proposed method are verified in real data evaluation.

With the requirements of time-sensitive and highly reliable applications, it is difficult to implement emerging communication technologies in internet of vehicles. Literature works are dedicated to improving the reliability and efficiency of data delivery, to address problems such as high and unstable End-to-End (E2E) latency, high network overhead. This paper explores the routing protocol with assistance of core network to provide high-reliability and low-latency data delivery services. We propose a Multi-dimension and Priority-based Vehicle-Road Collaborative Routing (MP-VRCR) protocol to improve the efficiency and reliability of data delivery. Specifically, MP-VRCR utilizes multi-dimension indicators to evaluate the data delivery capability of candidate devices, considering trajectory similarity of data, predicted buffer time, buffer occupation and distance cost. Furthermore, MP-VRCR evaluates device priority based on geographical information, selects optimal next-hop device, and evaluates data priority to order data forwarding. Then, MP-VRCR updates the congestion status of network relay devices according to buffer occupation. Vehicles carry data bypass congested network relay devices to reduce the E2E latency. Extensive simulation results show that MP-VRCR significantly outperforms other baseline algorithms regarding delivery ratio, overhead, average delivery latency.

Multiview spectral clustering, renowned for its spatial learning capability, has garnered significant attention in the data mining field. However, existing methods assume that the optimal consensus adjacency matrix is confined within the space spanned by each view’s adjacency matrix. This constraint restricts the feasible domain of the algorithm and hinders the exploration of the optimal consensus adjacency matrix. To address this limitation, we propose a novel and convex strategy, termed the consensus neighbor strategy, for learning the optimal consensus adjacency matrix. This approach constructs the optimal consensus adjacency matrix by capturing the consensus local structure of each sample across all views, thereby expanding the search space and facilitating the discovery of the optimal consensus adjacency matrix. Furthermore, we introduce the concept of a correlation measuring matrix to prevent trivial solution. We develop an efficient iterative algorithm to solve the resulting optimization problem, benefitting from the convex nature of our model, which ensures convergence to a global optimum. Experimental results on 16 multiview datasets demonstrate that our proposed algorithm surpasses state-of-the-art methods in terms of its robust consensus representation learning capability. The code of this article is uploaded to https://github.com/PhdJiayiTang/Consensus-Neighbor-Strategy.git.

Finite mixture models based on the symmetric Gaussian distribution have been applied broadly in data analysis. However, not all the data in real-world applications can be safely supposed to have a symmetric Gaussian form. This paper presents a new mixture model that includes the inverted Beta mixture model (IBMM) as a special case to analyze the positive non-Gaussian data. The advantage of the proposed model is that the number of the model parameters is variable and infinite. Consequently, the proposed model is adaptable to the size of the data. On the basis of the recently proposed extended variational inference (EVI) framework, we develop a closed-form solution to approximate the posterior distributions. The performance and the effectiveness of the proposed model are demonstrated with the real data generated from two challenging applications, namely, image classification and object detection.