Detecting blended Interest Flooding Attacks (IFA) and Collusive IFA (CIFA) in Named Data Networking (NDN) remains challenging due to the feature masking effect, where persistent IFA loads and transient CIFA pulses non-linearly overlap. Existing methods struggle to disentangle these superimposed signatures due to computational bottlenecks or limited temporal resolution. In this paper, we propose MST-Mamba, a unified detection framework built upon the Selective State Space Model (SSM). Our core innovations include a Multiscale Temporal Awareness (MTA) module to capture heterogeneous attack dynamics and a self-supervised pre-training strategy to eliminate topological interference. Experimental results demonstrate that MST-Mamba achieves over 99% accuracy with O(L) linear inference complexity. Even in complex blended scenarios, the model exhibits exceptional cross-topology generalization and low latency, providing a robust technical paradigm for securing NDN infrastructures.

Vehicular named data networking (VNDN), which integrates the principles of named data networks with vehicular ad hoc networks, represents a promising paradigm for future intelligent transportation systems. Nevertheless, VNDN faces significant hurdles, including broadcast storms from excessive interest packet flooding and reverse-path disruptions due to high vehicular mobility. To address these challenges, we introduce an adaptive forwarding with path optimization method. First, a dynamic caching algorithm is designed to optimize roadside unit storage efficiency and maximize cache hit rates. Second, a gated recurrent unit-based adaptive data forwarding mechanism is introduced to dynamically select optimal forwarders and preserve reverse paths via decentralized heartbeat detection and interface remapping, improving link reliability. Simulation outcomes demonstrate that the proposed approach significantly lowers data retrieval delays while curbing overall communication overhead.

Currently, Unmanned Aerial Vehicle (UAV) networks demonstrate significant application value in time-sensitive scenarios, such as disaster rescue; however, the dynamic nature of UAV network topology leads to challenges including high communication delay and energy consumption control. To address this problem, this study proposes an energy-efficient tree-chain data sharing scheme with Named Data Network (NDN). First, a two-stage multi-objective grey wolf optimization algorithm (TMOGWO) is developed, incorporating a novel observation phase to enhance population diversity and avoid local optima. These improvements bolster clustering stability, optimize the dynamic tree-chain structure, shorten communication paths, and reduce energy consumption. Second, a dynamic pricing-based preference game matching algorithm (PMGDP) is proposed to optimize cross-cluster resource allocation and maximize system utility through dynamic price adjustment, reputation preference, and stable matching mechanisms. Additionally, the hierarchical caching strategy and interest packet forwarding mechanism of NDN are utilized to improve cache hit rate and minimize redundant transmissions. Compared to benchmark schemes such as HKMeans-NDN, LEACH-BC, and PSO-NDN, experimental evaluations demonstrate that this scheme achieves the maximum improvements of 6% in energy consumption reduction, 33.3% in cache hit rate enhancement, and 33% in communication latency reduction.

It is crucial to mine latent sentiments and opinions from comments on social media to comprehensively understand people's preferences and experiences. Aspect-based sentiment analysis aims to identify the sentiment polarities for specific aspects of a sentence. Mainstream methods generally struggle to process sentences with complex syntactic structures and multiple aspects of different sentiment polarities. To overcome this challenge, this article proposes a dual-channel and aspect-aware graph convolutional network (DAGCN) model, which fully utilizes the syntactic and semantic information to accurately capture the sentiment features for specific aspects. Specifically, a syntactic graph convolutional network module is designed to effectively learn syntactic information by constructing an aspect-oriented dependency tree and employing a gated aggregator. To reduce the semantic interference from multiple aspects, a semantic graph convolutional network module is developed to capture both local and global semantic correlations corresponding to specific aspects. Furthermore, a BiAffine module is integrated to facilitate the interaction between the syntactic and semantic information. Experiments on four benchmark datasets illustrate that our proposed DAGCN significantly outperforms state-of-the-art baselines.

There are many interference factors in the real world that are not related to facial expressions, such as background, lighting intensity, and changes in image resolution. In order to address the problem of facial expression recognition, this article proposes a fuzzy neural network enabled deep subspace domain adaptive fusion (FNN-DSDAF) by integrating the fuzzy logic module into the convolutional network structure. The fuzzy logic module integrates the powerful learning ability of deep networks can learn a set of robust Gaussian membership functions through a series of learnable Gaussian membership functions and logical operations. When training the classical normalized exponential loss function to force different categories of samples to maintain a certain distance in the learned feature space, FNN-DSDAF further uses the local hold loss function to make the local clusters within each category of samples more compact. The experimental results demonstrated that the fuzzy neural network had good feature decoupling ability and was suitable for facial expression understanding in real-world scenarios, and improve the accuracy, reliability, and robustness.

As a key application in the era of 5G communication, the demand for Quality of Service (QoS) in vehicle networks continues to increase, cloud-edge-end collaborative computing offloading has become a key technology, enabling vehicles to offload computing intensive tasks to edge servers or cloud servers to reduce their computing burden. Traditional algorithms often fail to jointly optimize energy consumption and latency while suffering from slow convergence. This paper proposes an Evolving Alternating Direction Multiplier Method (EADMM) aimed at enhancing user performance and optimizing computational offload strategies. Firstly, the EADMM algorithm takes into account the weights of delay and energy consumption, with the goal of minimizing the total cost of users. Then, it combines predictor-corrector to adjust the Lagrange multipliers to accelerate iteration. Finally, the convergence speed of EADMM algorithm is enhanced through dynamic penalty term updates. The experimental result shows that our proposed EADMM algorithm enhances the convergence and robustness of traditional algorithms, while reducing communication costs and computational delays.

With the development of the Internet of Vehicles (IoV) industry, the introduction of cloud-edge collaboration has greatly enhanced the computing capabilities of vehicle networks. However, optimizing computing offloading and resource allocation strategies in IoV to reduce latency and energy consumption at the vehicle terminals remains a challenge. This paper proposes an Adaptive Computing Offloading and Resource Allocation Strategy (ACORAS) for IoV based on cloud-edge collaboration. Firstly, a Vehicles-Collaborative Road Side Units-Cloud (VCRSUC) system architecture is constructed by considering the use of idle resources on edge servers at remote Road Side Unit (RSU) to reduce the total cost at the vehicle terminals. Secondly, the discrete particle swarm optimization algorithm is combined with chaotic mapping and Cauchy mutation, and dynamically adjusts weights and learning factors based on variable updates. Finally, our proposed ACORAS gradually approaches the optimization of the computing offloading decisions and resource allocation decisions through iterative calculations. Simulation results show that our proposed ACORAS can effectively reduce the total cost while considering latency and energy consumption, demonstrating superior performance compared to traditional algorithms.

Vehicle-to-Everything edge computing accomplishes the goal of low latency by offloading tasks to edge computing servers, but how to reduce the computing latency of vehicle terminals while ensuring low energy consumption and load balance of servers is still a challenge. In order to address this issue, this paper proposes an adaptive computation offloading strategy based on Adaptive Alternating Direction Method of Multipliers (AADMM). Firstly, a distributed framework for multiple vehicles and multiple Road Side Units(RSUs) is constructed by comprehensively considering the weights of delay and energy consumption, with the optimization objective of minimizing the total system cost. Secondly, the original variables and dual variables are updated alternately, and the step size is dynamically adjusted based on the magnitude of variable updates, thereby progressively approaching the optimal solution. Finally, simulation experiments show that our proposed strategy can effectively reduce the system cost compared with other traditional algorithms under the comprehensive consideration of delay and energy consumption, and our proposed algorithm has better performance in terms of number of vehicles, speed of vehicles, size of the task, etc.

The rapid increase of the number of motor vehicles over the last several decades has driven a corresponding increase in the severity of traffic congestion, which has exhibited a considerable human impact. Intelligent anti-collision control via inter-vehicle communication technology can facilitate vehicles adhering to spacing speed standards and improve road utilization and traffic efficiency. In this paper, we apply a deep learning image estimation model based on joint attention mechanism. The network framework uses a deep estimation network Yolov5 and a location based VANET information fast transmission strategy to work together. This paper mainly considers vehicle distance measurement technology as a point of entry to study multi-sensor information fusion for vehicle collision prevention technology based on the self-organizing vehicular ad-hoc network (VANET). This paper also proposes a solution based on the strategy of one-way transmission of shared information and dynamic valuation of a cluster distance threshold with vehicle density. The proposed vehicle anti-collision control algorithm is designed to realize dynamic vehicle control via inter-vehicle communication. In this paper, the two-way coupling of traffic flow and network simulator is used to randomly generate vehicle nodes on the road, and the behavior of the anti-collision system is simulated. The experimental results show that the predetermined control goal is achieved, which demonstrates the effectiveness of the proposed algorithm.

As a direct application of 5G communications and computer technology, Vehicular Ad-Hoc Networks (VANETs) are already having a profound impact on all sectors of society. However, frequent changes in the topology of VANETs have resulted in poor vehicle communication quality, highly susceptible to communication link breaks and data transmission reliability decreases, and the cost of vehicular communication increases continuously. In this paper, an Adaptive K-medoids based on Greedy Perimeter Stateless Routing (AK-GPSR) algorithm is proposed in multi-channel vehicular network communication of urban scenario. It is an unsupervised learning algorithm, aiming to form high-quality link communication, more stable network topology, and improved data information transmission reliability. First, the proposed AK-GPSR algorithm applies Gap statistic to evaluate the K-medoids algorithm and select the best K value. Further, the K-medoids algorithm clusters the vehicles in the simulation area by the optimal K-value to divide the K clusters. Finally, the packets are forwarded from the source vehicle to the destination vehicle or Road Side Unit (RSU) using the forwarding method of the Greedy Perimeter Stateless Routing (GPSR) algorithm. The experimental results show that our proposed AK-GPSR algorithm has good performance and applicability in multi-channel vehicular network communication.

The customized bus operating mode based on passenger demand is an effective way to solve the problem of bus services in low travel density areas such as urban fringe areas, ensure the profitability of bus enterprises, and promote the development of customized bus and other emerging bus. First, this study introduces the concept and operating principle of customized bus, determines the advantages and disadvantages of customized bus, evaluates the relevant theories of customized bus lines and station planning, and determines the principles of customized bus lines and station planning. Second, according to the characteristics of customized bus, this study proposes a novel customized bus line and station planning method completely based on passenger travel demand, including travel demand data processing, traffic community division, joint station planning, the establishment of a customized bus line planning model, and the solution of the planning model. Finally, the proposed planning method and improved ant colony optimization and clustering are verified by simulation experiments. The experimental results show that the station line planning method proposed in this paper can better realize the line planning of demand-responsive customized bus as well as meet diverse passenger travel needs.

With the increasing demand for intelligent transportation systems, short-term traffic flow prediction has become an important research direction. The memory unit of a Long Short-Term Memory (LSTM) neural network can store data characteristics over a certain period of time, hence the suitability of this network for time series processing. This paper uses an improved Gate Recurrent Unit (GRU) neural network to study the time series of traffic parameter flows. The LSTM short-term traffic flow prediction based on the flow series is first investigated, and then the GRU model is introduced. The GRU can be regarded as a simplified LSTM. After extracting the spatial and temporal characteristics of the flow matrix, an improved GRU with a bidirectional positive and negative feedback called the Bi-GRU prediction model is used to complete the short-term traffic flow prediction and study its characteristics. The Rectified Adaptive (RAdam) model is adopted to improve the shortcomings of the common optimizer. The cosine learning rate attenuation is also used for the model to avoid converging to the local optimal solution and for the appropriate convergence speed to be controlled. Furthermore, the scientific and reliable model learning rate is set together with the adaptive learning rate in RAdam. In this manner, the accuracy of network prediction can be further improved. Finally, an experiment of the Bi-GRU model is conducted. The comprehensive Bi-GRU prediction results demonstrate the effectiveness of the proposed method.