Transactions on Emerging Telecommunications Technologies最新文献

The Internet of Things (IoT) is particularly vulnerable in this new era, as IoT devices often rely on lightweight encryption and security measures due to their limited processing capabilities and power constraints. Existing signature-based intrusion detection strategies are inadequate against the advanced and adaptive nature of quantum attacks, which can exploit the polymorphic and metamorphic behavior of quantum-enhanced malware. This research addresses the challenges posed by a possible attack scenario named the “Quantum-Enhanced Cloak Malware (QECM) Attack” and aims to provide enhanced protection to IoT systems against this sophisticated threat. The proposed “Intelligent Swift Scan Quantum-Resilient Intrusion Detection System (ISS-QR-IDS)” integrates advanced techniques to enhance detection speed and accuracy, mitigate risks associated with polymorphic and metamorphic malware behaviors, and secure communication channels against quantum threats. The model incorporates the Parallel Vario-Isolation Detector, which combines Variational Autoencoders (VAEs) and Parallelized Isolation Forests to detect quantum-enhanced malware, and Hypergraph Attention Networks (HGA-Net), leveraging Hypergraph Neural Networks (HGNNs) and Graph Attention Networks (GATs) to detect the critical interactions and improve anomaly detection accuracy. Additionally, postquantum cryptographic algorithms like NTRU Encrypt and FALCON ensure secure communication channels and data integrity. By combining these advanced techniques, ISS-QR-IDS aims to provide a robust defense mechanism against sophisticated cyber threats targeting IoT networks, ensuring their security and resilience in the face of quantum computing advancements.

To address the challenges of false negatives and false positives of small objects and the difficulty of fine-grained behavior recognition in complex traffic scenarios, this paper constructs a hybrid deep learning framework based on image detection to synergistically improve multi-object localization accuracy and semantic understanding capabilities. The framework first uses a combination of Gaussian and bilateral filtering for denoising, enhancing input quality and improving detection sensitivity for small objects. In the detection phase, the YOLOv5s (You Only Look Once 5s) model is used as the baseline. The Convolutional Block Attention Module (CBAM) attention mechanism is applied to enhance the representation of key features. K-means clustering is used to adaptively generate prior anchor boxes that match the scale distribution of objects in traffic scenarios. The CIoU (Complete Intersection over Union) loss function is also used to optimize bounding box regression accuracy, improving small object detection performance while maintaining model lightweight. To achieve fine-grained semantic understanding, a two-branch classification network is designed. The attribute branch uses the ConvNeXt-Tiny (Convolutional Next-Generation Tiny) structure to extract static appearance features, while the event branch utilizes the nonlocal operations module to capture dynamic contextual dependencies. Weighted fusion of these two features enables joint recognition of attributes and behaviors. A GNN-CNN (Graph Neural Network-Convolutional Neural Network) hybrid classification module is also constructed. The GNN models the spatiotemporal interactions between vehicles, while a lightweight CNN extracts local texture features. These features are adaptively fused using the Squeeze-and-Excitation (SE) attention mechanism, and a softmax classifier performs traffic behavior judgment. Experiments show that the YOLOv5s-CBAM model achieves a mean average precision (mAP) of 0.55 for detecting extremely small objects (< 16 × 16). In the overloaded vehicle detection task, the GNN-CNN module achieves accuracy and recall of 0.92 and 0.90, respectively. This hybrid deep learning framework provides reliable technical support for automated traffic inspections. It improves the accuracy and stability of small object detection and fine-grained event recognition in complex traffic scenarios. Its modular design and strong scalability make it widely applicable and conducive to promoting intelligent transportation towards higher levels of automation.

In recent years, cloud computing has gained enormous development in computing systems. The cloud computing environment provides diverse benefits to its cloud users via the internet including storage, applications, on-demand services, etc. Nowadays, it has been accepted and utilized by various companies for uploading their massive amounts of data to a cloud platform. As a result, various cloud computing-based IDS (CIDS) techniques are developed to prevent a cloud network from attacks and protect the data from internal and external anomalous activities. Despite that, security and privacy concerns remain a significant challenge, which demands an effective methodology to ensure user confidentiality and integrity. Thus, we proposed a Stacked Convolutional and Recurrent Contractive Sparse Autoencoder (SCRCS-AE) and Levy flight and reconstructed mathematical optimization acceleration-based Arithmetic Optimization Algorithm (LRMOA-AOA) for an efficient ID in a cloud environment. In this paper, we stack three SCRCS-AE blocks to extract their features. A single SCRCS-AE block involves a convolutional encoder and recurrent decoder to capture long-term dependencies and rebuild input in forward and reverse directions for excellent feature extraction and classification of different intrusions. The integration of sparse and contractive loss is deployed to extract high-dimensional data features to boost the SCRCS-AE model's generalizability and robustness. The LRMOA-AOA optimization algorithm integrates a Levy flight distribution and arithmetic optimization algorithm (AOA) approach that tunes the hyperparameters to enhance the efficacy of the SCRCS-AE. The proposed SCRCS-AE achieved 98.73% accuracy, 98.46% detection rate, 1.27% false alarm rate, and 98.61% precision on the UNSW-NB15 dataset and attained 97.93% accuracy, 97.74% detection rate, 2.07% false alarm rate, and 97.59% precision on the NSL-KDD dataset. These superior outcomes show that the proposed SCRCS-AE technique works well on CIDS in detecting diverse assaults with higher detection rates and lower false alarm rates to strengthen cloud network security and privacy.

The Internet of Vehicles (IoV) collects real-time data on traffic, environmental conditions, and vehicle behavior through vehicle interconnection and interaction with infrastructure, providing support for the of Machine Learning (ML) in intelligent decision-making. However, centralized learning approaches suffer from issues like privacy leakage and high communication costs. Federated Learning (FL) addresses these issues by sharing local model updates, but in IoV environments, challenges such as data heterogeneity result in slow convergence, limited communication resources, and security threats like gradient leakage. To tackle these challenges, this paper proposes Adaptive Blockchain-based Hierarchical Federated Learning with Gradient Alignment (ABHFL). ABHFL groups vehicle nodes and RSUs into a hierarchical structure to perform local training, gradient alignment, and model aggregation at different levels. The proposed Adaptive Gradient Alignment (AGA) mechanism aligns the update directions of nodes towards the global optimal direction through multiple rounds of alignment after local gradient computation, accelerating model convergence and ensuring that the gradients uploaded contribute positively to global optimization. In addition, a lightweight Proof-of-Gradient-Alignment (PoGA) consensus mechanism is designed, which performs two-stage verification of the uploaded gradients and integrates reputation scores and blockchain storage to guarantee gradient reliability and protect against attacks. Extensive experiments demonstrate that ABHFL significantly improves model convergence, communication efficiency, and security reliability, providing an effective and robust solution for FL in IoV scenarios.

In general, multi-user multiple input multiple output orthogonal frequency division multiplexing (MU-MIMO-OFDM) allows multiple users to interconnect to a base station simultaneously using OFDM modulation and various antennas. However, managing resources like energy and minimizing delays is difficult, requiring smart solutions for smooth operation and better performance. Thus, a joint power and delay optimization based resource allocation using Enhanced Elman Spiking Sparse Graph Networks (EESS-Gnet) with Humboldt Squid Optimization Algorithm (HSOA) and Reuse-Based Online Joint Routing Scheduling Optimization (ROJR) (EESS-GNet-HSOA-ROJR) in MU-MIMO-OFDM system is proposed in this manuscript. The proposed mechanism is performed in two stages that are power allocation and delay optimization. The goal of the first phase is to maximize throughput by allocating network resources to user equipments (UEs) based on transmission rate and power through an EESS-Gnet. In order to reduce the loss function, HSOA is proposed to optimize the layers of EESS-Gnet. In the second stage, ROJR is proposed for optimizing delay in the MU-MIMO-OFDM system. In the ROJR approach, the delay bound value is estimated by scheduling the transmission flows in the channel. The simulations of EESS-GNet-HSOA-ROJR were conducted using MATLAB software. The suggested resource allocation algorithm's performance is assessed and contrasted with the current method of measuring different QoS metrics, including throughput, delay, fairness index, power consumption, spectrum capacity, and loss rate. Thus, the proposed approach has attained 26.46%, 23.09%, and 21.98% higher throughput, 29.78%, 26.86%, and 20.25% improved energy efficiency, 17.45%, 15.98%, and 14.02% lower processing time, and 27.89%, 34.87%, and 23.56% lower loss rate than other conventional approaches like PDO-URA, PCO-OBT, and ADNN-ALSTM-TRDA methods respectively.

Voice over Internet Protocol (VoIP) has emerged as a game-changing communication technology given that it allows for low-cost long-distance conversations with plenty of additional benefits. In this era of cloud computing, VoIP can offer even cheaper calls and scalable services with the help of virtualized telephone infrastructure. The integration of virtualized telephone infrastructure with VoIP is known as “cloud-based VoIP.” In this paper, we investigate a cloud-based VoIP under the advanced persistent threat (APT) attack. An APT attack is a sophisticated type of cyberattack that tries to steal personal information by staying in the infected system for an extended period of time, thereby impacting the system dependability. “Dependability is a measure of a system's availability, reliability, maintainability, and in some cases, other characteristics such as durability, safety and security”. Hence, we develop a robust mechanism for mitigating APT attack in a cloud-based VoIP phone system and investigate its dependability to minimize the aftermaths of the attack. We employ a semi-Markov process (SMP) model to study the dependability as it gives consideration to the non-Markovian nature of the holding times of various system states. The SMP model is then used to analyze both the time-dependent behavior and the long-term (stationary) performance characteristic of the cloud-based VoIP system, specifically in terms of availability, reliability, and confidentiality. Numerical results are displayed graphically, and the proposed dependability model is supported by stochastic simulation. It has been established from the numerical results that the cloud-based VoIP is the most sensitive and critical when it is exploited by cyberattacks, and the lifetime of the system can be extended if the weaknesses of the system are discovered before it is exploited by the attackers.

The scalability, adaptability, and pay-per-use nature of cloud computing have contributed to its meteoric rise to prominence, enabling customers to access services regardless of their physical location. A major obstacle to effective resource management is the wide variety of services provided and the wide variety of user needs. Due to inadequate resource use and suboptimal scheduling tactics, cloud data centers, which consist of physical machines (PMs) hosting many virtual machines (VMs), frequently experience significant energy consumption. A task scheduling technique is introduced in this study to tackle energy efficiency in cloud environments. It integrates two meta-heuristic algorithms. A Slack-based classification algorithm is used first to cluster tasks and then rank them according to their criticality. In order to schedule vital work, we use the Remora Optimization Algorithm (ROA). For noncritical jobs, we use Particle Swarm Optimization (PSO). Researchers tested several configurations ofVMs and job counts in an experimental setting and then compared the outcomes to those of more conventional approaches like Genetic Algorithm (GA) and baseline PSO. The proposed approach shows promise as an efficient scheduling approach for environmentally conscious cloud computing, thanks to its substantial reductions in execution time and energy consumption. Evaluations were carried out in a simulated cloud environment, incorporating different task counts and VM configurations. The proposed mechanism underwent a comparative analysis with eight benchmark methods. The findings indicate that the proposed method shows a marked superiority over current techniques, realizing a 33.5% decrease in execution time (168.57 s compared to 253.47 s) and an 11%–52% enhancement in energy efficiency (0.653 kWh vs. a maximum of 0.852 kWh). The results validate the efficacy of the scheduling strategy in improving energy efficiency and performance within cloud computing environments.

Vehicular ad hoc networks (VANETs) enable real-time communication but are vulnerable to security threats, particularly distributed denial of service (DDoS) attacks, that cause delays and network failures. Traditional static detection systems struggle to adapt to dynamic traffic conditions. To address this problem, we propose ELITE, a lightweight and intelligent DDoS detection framework designed for secure VANETs. ELITE employs a three-layer architecture featuring a random fuzzy tree (RFT) classifier, which combines the speed of decision trees with adaptive fuzzy reasoning for efficient anomaly detection. It also includes a latency-aware scheduling system that ensures the urgent traffic is handled, while a few essential requests are sent to nearby edge servers or to the cloud. This work has three distinct contributions: integration of X and Y into one intelligent smart-environment architecture, like edge-cloud optimization model 96% stability of delay-sensitive edge-cloud optimization, and development of a lightweight threat detection module with improved accuracy and real-time capability. Experimental results demonstrate that ELITE achieves a high detection accuracy of 95.7%, effectively adapts to traffic changes, reduces false positives, and improves latency performance.

Over the last few years, cloud computing has emerged as the best option for offering various applications. It can supply databases, web services, processing, storage, development platforms, and web services to help businesses swiftly expand their infrastructure and service offerings. However, massive amounts of data will severely burden the cloud computing environment. Due to this, load-balanced task scheduling has remained a crucial aspect of resource distribution from a data center, ensuring that each virtual machine (VM) has a balanced load to fulfill its full potential. Overloading or underloading a host or server can cause issues with processing speed or even cause a system crash. To prevent this, we need an intelligent way to schedule tasks. Therefore, the hybrid optimization algorithm called gazelle coati optimization algorithm (GCOA) is introduced in this paper to schedule tasks in a cloud environment. This algorithm integrates the coati optimization algorithm (COA) and the gazelle optimization algorithm (GOA) to enhance the GOA's exploitation process. The main objective of this hybrid approach is to optimize scheduling, maximize VM throughput and resource utilization, and establish load balancing between VMs based on makespan, energy, and cost. The performance assessment of the proposed approach is conducted on two real-world workloads, such as Google Cloud Jobs (GoCJ) and the heterogeneous computing scheduling problems (HCSP) datasets, using several performance metrics, and the results are compared with the previous scheduling and load balancing methods. The experiment results show that the suggested strategy produced significant gains in makespan, energy, cost, resource utilization, and throughput—up to 10% and 60%, respectively—making it appropriate for real-world cloud infrastructures.