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.

Owing to the harsh underwater environment and limited energy replenishment, extending network lifetimes and achieving energy efficiency are critical challenges in underwater wireless sensor networks (UWSNs). In this paper, a novel multiobjective Arctic puffin optimization (MOAPO) method that is specifically tailored for clustering and routing in UWSNs is proposed. Within the proposed MOAPO framework, K-means++ clustering is first used to optimize the initial cluster head (CH) positions, thereby improving clustering performance and ensuring more effective coverage and resource utilization. A feedback-based mechanism is used to adaptively adjust the behavior conversion factor to balance global exploration and local exploitation, thereby avoiding premature convergence and improving the quality of the selected CHs. A fitness function that incorporates the node energy, communication distance, and CH selection frequency is constructed, with the weights dynamically tuned according to the current energy state of the network, which results in energy-efficient and energy-balanced CH selection. Based on the selected CHs, a multiobjective routing strategy in which the energy levels, delays, and packet loss rate are considered is applied to construct reliable data transmission paths. The simulation results confirm that compared with existing methods, the MOAPO method achieves lower energy consumption and a longer network lifetime, thus demonstrating superior robustness and adaptability.

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.

With the widespread adoption of wearable devices, human activity recognition (HAR), which is an essential branch of multivariate time series classification (MTSC), has been broadly applied in areas such as intelligent health monitoring, smart homes, and sports training. Traditional time series processing methods focus on time-domain features and often neglect frequency-domain characteristics; however, much of the critical information related to human activities is usually reflected in the frequency components of signals. To address this issue, we propose a Fourier transform convolution fusion module for extracting frequency-domain features and integrating them with time-domain features. Based on this module, we further propose an innovative Fourier transform convolution network (FTCNet). FTC-Net efficiently integrates time-domain and frequencydomain features by combining the Fourier transform convolution fusion module with an efficient channel attention block (ECA block), which enhances the model's ability to extract key features. The experimental results show that FTC-Net achieved excellent classification performance across 11 HAR-related datasets, with an average accuracy of $87.85 \%$, significantly outperforming current mainstream models. The results of ablation studies also demonstrate the positive impact of frequency-domain features on model performance. FTC-Net offers a new solution for complex human activity recognition tasks and opens new research directions for integrating time-domain and frequency-domain features.

Vehicular Named Data Networking (VNDN) is a communication architecture that applies the concepts of Named Data Networking (NDN) to Vehicular Ad-hoc Networks (VANETs). In VNDN, reliable data transmission under vehicular mobility conditions is critical for Quality of Service (QoS) support. To address the challenges of broadcast storms and unstable communication links in VNDN, this paper proposes a Prediction-based Link Adaptive Forwarding Method (PLAFM), which effectively mitigates reverse path disruptions while reducing Interest packet flooding. Experimental results demonstrate that PLAFM significantly improves communication efficiency and data delivery reliability.

Smart cities have entered into a new era with the widespread commercialization of 5G. As a key supporting technique for smart cities, Internet of everything (IoE) puts forward higher requirements for real-time data and massive access. In this article, we propose a novel embedded cognitive radio (CR) enabled rate splitting multiple access (ECR_RSMA) solution, in which the secondary user (SU) can access more than one idle spectrum holes of the primary users (PUs) by spitting the data rate without causing additional interference to the PUs. To ensure the quality of service (QoS) of each user, a composite successive interference cancellation (SIC) decoding scheme is specially designed, and the channel prediction algorithm is employed to combat the channel aging effect. The effectiveness of the proposed ECR_RSMA is verified through computer simulations. Compared with the state-of-the-art MA schemes, such as CR_NOMA, the proposed solution can significantly improve the performance in terms of the spectrum efficiency as well as the outage probability.

Vehicular named data networking (VNDN) is a new type of network architecture based on named data networking(NDN) in vehicular ad hoc networks (VANETs), Considering that the high-speed mobility of vehicle nodes in VANET makes data transmission difficult, we propose a clustering data transmission approach and establish intra-cluster and inter-cluster communication. Simulation results show the effectiveness of the proposed approach.

In Named Data Networks (NDNs), the Collusive Interest Flooding Attack (CIFA) is a new variant of the Interest Flooding Attack (IFA). Due to the low-rate intermittency of a CIFA, it is more stealthy and deceptive than an IFA, posing challenges for most IFA detection schemes to effectively differentiate between normal and anomalous traffic. The use of machine learning algorithms to identify the characteristics of network traffic makes it possible to more accurately distinguish between normal network behavior and attacks. Based on this, to better detect CIFAs, we propose a Radial Basis Function (RBF) neural network-based scheme that can quickly detect CIFAs by identifying network traffic features and classifying them using an RBF neural network algorithm. The results show that the method has better detection performance than classical detection methods.

Named data networking (NDN) has been regarded as a promising scheme for next-generation network architecture, with network security remaining a key issue. In NDN, a new Distributed Denial of Service (DDoS) attack model called the interest flooding attack (IFA) has emerged and poses a serious threat to NDN. In addition, to increase the complexity of detection and defense against attacks, a variant of the IFA called the collusive interest flooding attack (CIFA) has emerged recently. Although many IFA and CIFA detection methods have been proposed, most existing countermeasures focus solely on detecting either IFA or CIFA. More importantly, to the best of our knowledge, there is no research on effective detection scheme for scenarios where IFA and CIFA coexist in a blending attack yet. In this paper, motivated by the concept that network traffic exhibits time series characteristics, we propose an attack identification and detection scheme based on WEASEL (Word ExtrAction for time SEries cLassification) aimed at accurately identifying and detecting the blending IFAs in NDN.

A wireless body area network (WBAN) is a crucial technology for implementing intelligent health monitoring. Traditional WBANs focus on the monitoring and classification of single physiological signals, which cannot meet the comprehensive requirements for monitoring human health and behavior. This paper proposes a local feature channel fusion convolutional neural network (CNN) model that can monitor and analyze multiple physiological signals collected by WBANs and can be used for disease identification and human activity recognition. The model employs convolution kernels to perform convolution operations on each channel, extracting local features of each channel and then performing channel fusion convolution operations to effectively integrate information between different channels. Additionally, the model incorporates an attention mechanism to dynamically adjust feature weights, highlight important features, and suppress redundant information. Experiments conducted on 11 WBAN-related datasets from the UEA database demonstrate that the proposed model achieves optimal classification performance.

Summary Neural networks are becoming increasingly widely used in various fields, especially for academic risk forecasts. Academic risk prediction is a hot topic in the field of big data in education that aims to identify and help students who experience great academic difficulties. In recent years, the use of machine learning algorithms and deep learning algorithms to achieve academic risk prediction has garnered increased attention and development. However, most of these studies use nontime series data as features for prediction, which are slightly insufficient in terms of timeliness. Therefore, this article focuses on time series data features that are more expressive of changes in students' learning status and uses multivariate time series data as predictive features. This article proposes a method based on multivariate time series features and a neural network to predict academic risk. The method includes three steps: first, the multivariate time series feature is extracted from the interaction records of the students' online learning platforms; second, the multivariate time series feature transformation model ROCKET is applied to convert the multivariate time series feature into a new feature; third, the new feature is converted into a final prediction result using a linear classification model. Comparative tests show that the proposed method has high effectiveness.

Underwater acoustic sensor networks (UASNs) currently provide an important technical means of underwater communication, but there are difficulties in power updating or power replenishment because the sensor nodes work in an underwater environment. Therefore, energy consumption optimization has become the focus of research on UASNs. Node clustering is widely considered to be able to optimize network energy consumption. Although the current clustering-based routing method prolongs the network life cycle to a certain extent, some nodes may fail due to excessive energy consumption caused by excessive data transmission tasks, and the problems of high and uneven energy consumption still exist. To extend the life cycle of UASNs, this article proposes an unequal clustering method based on particle swarm optimization. Our method uses iterative updates to dynamically adjust the cluster size based on the remaining energy of the cluster head, the distance from the cluster head to the Sink node, and the number of times the cluster head forwards data between clusters to balance the cluster head load. The energy consumption of the whole path from the cluster head to the Sink node and the number of hops required are considered in the intercluster transmission phase. Simulation results show that this method can effectively reduce the network energy consumption.

The underwater acoustic sensor network (UASN) is a specific deployment of Internet-of-Things (IoT) technology in the underwater environment, since energy constraints limit the lifetime of UASNs, effectively balancing the energy consumption of acoustic sensor nodes in UASNs is important to maximize the amount of information collected and to prolong the network lifetime. Node clustering is widely regarded as one of the most important energy-efficient schemes for UASNs. However, most existing clustering schemes focus on the cooperation-based election of cluster headers (CHs) in a centralized manner. Due to the limited energy capacity, acoustic sensor nodes are designed to save their own energy, hindering the realization of such cooperation. To address this issue in this article, game theory is applied to UASNs to balance network energy consumption and model acoustic sensor nodes as rational and selfish players. Specifically, a game-theory-based clustering (GTC) scheme for UASNs is developed. In the CH election phase, each node makes a decision in pursuit of a greater payoff based on the Nash equilibrium. An incentive mechanism is invented to induce nodes to make more beneficial collective decisions and plays a role in the CH rotation to effectively balance the energy consumption. Meanwhile, the network area is divided into nonuniform sectors to ensure the energy consumption of the CH is more evenly distributed. Simulation results show that the proposed GTC scheme can effectively balance network energy consumption and extend the network lifetime.

Named data networking (NDN) is a representation and implementation of an information centric network, which is considered as one of the next generation of network architectures. To the best of the authors’ knowledge, very few studies have considered multiple constrained quality‐of‐service (QoS) routing in NDN. In this study, a particle swarm optimisation‐forwarding information base (PSO‐FIB) algorithm that uses the forwarding experiences of particles to maintain the forwarding probability of each entry in the FIB is proposed. Illustrating the interaction of PSO‐FIB with the routing layer, the simulation results show that PSO‐FIB can support multi‐constrained QoS routing and achieve better performance in terms of successful delivery rate and average cost compared with random and ant colony optimisation strategies.