Transactions on Emerging Telecommunications Technologies最新文献

Cyberattack detection systems are critical in protecting the Internet of Things (IoT) network from attacks. Cyber-attack detection is critical in fog-based IoT systems to protect against a wide range of threats, such as illegal access, data breaches, and service disruptions. Present cyber-attack detection models have limitations, such as a high false alarm rate (FAR) and limited detection accuracy. Therefore, it is essential to create reliable cyberattack detection systems that can raise the accuracy of detection and lower the rate of false alarms. This paper presents a deep learning (DL)-based classifier model with an effective feature extraction mechanism for detecting cyber-attacks in a fog-based IoT system. Before initiating the process, data augmentation has been done to balance the dataset. Initially, the input data are collected from the public source dataset; the collected data are passed into the pre-processing stage to rescale the data properly, which is performed by Z-Score normalization. The optimal set of features from the pre-processed data is extracted by a meta-heuristic technique, namely the Hybrid white shark sea lion optimization algorithm (Hy-WS²LO). After selecting optimal features, the data is fed into a classifier model to detect cyber-attacks in a fog-based IoT environment. An effective Residual attention-based dilated pyramidal depth-wise separable convolution (ResA-D²PySepCo) is used for identifying cyber-attacks in fog-based IoT networks. The proposed model can obtain an accuracy of 98.87% and 99.74% for ToN IoT and CICIDS 2018 datasets.

Vehicular Ad-hoc Networks (VANETs) are a cornerstone of Intelligent Transportation Systems (ITS), enabling efficient vehicle-to-vehicle and vehicle-to-infrastructure communication. However, their open and dynamic nature makes them highly susceptible to security threats such as Distributed Denial of Service (DDoS) attacks and the injection of false data by malicious nodes. Existing security mechanisms often fall short in addressing these challenges due to the real-time and mobile characteristics of VANETs. This paper proposes a Hybrid Explainable Artificial Intelligence (XAI) framework integrated with a Swin Vision Transformer for robust intrusion detection in VANET environments. The proposed model leverages the Swin Transformer's hierarchical feature extraction capabilities and the interpretability of XAI to accurately classify network nodes based on behavioral and transmission characteristics. Key features such as packet transmission duration, communication regularity, and node status are analyzed to detect anomalies and differentiate between benign and malicious nodes. The inclusion of explainability allows for transparent decision-making, facilitating trust and understanding in critical automotive applications. Simulation results validate the model's effectiveness in detecting a wide range of attack vectors while maintaining high accuracy and low false-positive rates. This study contributes to the development of adaptive, intelligent, and trustworthy security solutions for next-generation vehicular networks operating in complex urban traffic scenarios.

In the modern era, blockchain technology integrated with the Internet of Things (IoT) has emerged as a powerful segment for secured and translucent smart city applications. Initially, authentication forms the stepping stone for defense in different types of information systems; earlier approaches used in the context of single-side centralization were found faint and uncertain, with the enlarged single-point failure owing to external vulnerabilities. With the incorporation of an advanced authentication scheme, this research proposes the Elliptic Key-modified Rivest Shamir Adleman (EKMRS) scheme to overcome the tackles in existing techniques, thereby improving the security of applications. Moreover, the risks involved in identity fraud can be effectively minimized by blockchain technology that assures that public keys are verified using a decentralized consensus technique and stored securely. This combination not only secures communication channels but also includes a decentralized ledger for identity verification with tamper-proof evidence. The EKMRS utilizes the Elliptic Curve Digital Signature Algorithm that enhances the scalability enlargement and robust solution for a smart city environment, ensuring strong authentication. The experimental results demonstrate the effectiveness of the EKMRS scheme by offering significant improvements in terms of metrics, achieving 0.029 ms for decryption time, an encryption time of 0.39 ms, and an information loss of 0.13 with the students mark sheet dataset over the other recognized approaches.

Recent years have seen tremendous progress in Autonomous Vehicle (AV) technology. Today, certain locations provide various levels of autonomy. Constrained environments, such as airports and golf clubs, may allow fully autonomous driving (AD). Meanwhile, many public roads have started providing Vehicle-to-Everything (V2X) capabilities, enabling AVs with driver intervention (DI). Yet, for future scenarios without DI, low-latency technologies, robust protocols, and secure communication mechanisms will be essential. However, dependence on computer algorithms introduces security concerns. A breach in an AV can result in serious injuries or even death. This issue requires next-generation vehicles to implement robust security solutions to prevent malicious attacks. This study discusses state-of-the-art security technologies, protocols, and organizations relevant to autonomous driving (AD) and autonomous vehicles (AVs). First, the paper explores concepts like V2X, Intrusion Detection Systems (IDS), and collaborative security measures. Then, it will discuss the state-of-the-art security studies from the perspective of misbehavior detection and collective perception. This survey fills a void in the literature (at the time of the writing). It is also a comprehensive V2X security guide on misbehavior detection, collective perception, and related technologies.

The advancement of collaborative autonomous driving relies on robust and efficient data exchange between vehicles and surrounding infrastructure. Vehicle-to-everything (V2X) fusion communication frameworks, built upon vehicular ad hoc networks (VANETs), enable the integration of heterogeneous data sources to enhance environmental perception and decision-making. However, practical implementation faces significant challenges due to communication interruptions inherent in dynamic VANET environments, leading to incomplete cooperative perception and increased safety risks. To address these challenges, this research proposes a V2X fusion communication framework, incorporating communication-interruption-aware cooperative perception, to ensure reliable information exchange for autonomous vehicles operating in collaborative scenarios. The framework leverages historical cooperation information to compensate for missing data caused by communication disruptions. Furthermore, a communication stochastic temporal convolutional networks (STCN) prediction model is introduced to extract critical features under varying network conditions, enhancing predictive accuracy for lost information. The data were collected from an open-source platform, which includes multi-agent sensor data (LiDAR, radar, and camera), global positioning system (GPS), and timestamped V2X messages simulating realistic vehicular traffic and environmental conditions under varying communication qualities. Packet drop rates were emulated to reflect real-world VANET communication inconsistencies. Additionally, knowledge distillation techniques provide targeted supervision to the predictive model, while curriculum learning strategies stabilize the training process under complex VANET scenarios. The results of the experiments prove that the proposed framework enhanced the perception reliability, and collaborative performance, communication reliability, decreased latency, enhanced obstacle detection accuracy, and decreased error results, including MAE (0.11) and MSE (0.12). This VANET communication architecture is a fusion-based framework that provides reliable, efficient, and safe data-driven collaboration within a group of autonomous vehicles.

Vehicular ad-hoc networks (VANETs) are crucial for road safety, traffic management, and intelligent transportation systems, but they are vulnerable to Distributed Denial of Service (DDoS) attacks, which can severely disrupt communication between vehicles and Roadside Units (RSUs). Traditional DDoS detection methods in VANETs are often inefficient due to reliance on centralized architectures and handcrafted features. To address these challenges, we propose the Hybrid Deep Learning with Federated Learning (HDL-FL) framework, which leverages Convolutional Neural Networks (CNNs) to capture spatial and temporal traffic patterns. By utilizing Federated Learning, HDL-FL enables distributed, privacy-preserving training across RSUs and vehicles while reducing communication overhead. Experimental evaluations in simulated VANET environments show that HDL-FL achieves a 94% improvement in accuracy, a 30% reduction in false positives, and a 99% increase in attack detection rate while also reducing communication overhead by 6.5 s and latency by 160 ms. The framework offers a scalable, robust, and privacy-preserving solution for securing next-generation Vehicle-to-Everything (V2X) infrastructures, outperforming traditional models in terms of spatio-temporal accuracy and scalability. For performance validation, the HDL-FL framework is compared with baseline models, including traditional machine learning approaches such as Support Vector Machine, AI, and IoT.

Securing the trustworthiness, privacy, and legitimacy of shared medical data in Wireless Body Area Network (WBAN) is a primary concern. Hence, a blockchain technology-based secure medical data storage scheme is developed in this paper. This developed model includes four primary phases. Before initializing, the WBAN data are collected. In the first phase, the user authentication is verified. For this purpose, the user's iris images are aggregated. These iris images are subjected to the Residual Attention Network (RAN). From the RAN, the user is authorized, and then security keys are given to the authorized user. Only after verifying the authentication of the user, the healthcare data is allowed to be stored in the blockchain. In the second phase, data sanitization takes place. The obtained WBAN medical data are sanitized using a data sanitization process with the optimal keys obtained from the Fusion of Golden Eagle and Eurasian Oystercatcher Optimization Algorithm (FGE-EOOA). Here, the data are encrypted by employing the Rivest-Shamir-Adleman (RSA) approach, and then encrypted medical data are stored in the blockchain. This ensures multi-step data security, which allows secure storage of WBAN healthcare data in the blockchain. While retrieving the stored data, the user authentication is verified on the user side, as well as in the same RAN model. This is the third phase of the developed model. When the user is proven to be an authorized one, the stored data in the blockchain corresponding to that particular user is retrieved. Using the data restoration process, which is the fourth phase of the developed model, the actual medical data is retrieved. If the user is unauthorized, then no access is provided to them. This ensures a multi-level of security for storing and retrieving data from the blockchain. The security offered by this model is evaluated and validated by contrasting and comparing it with other conventional data transfer methods.

Some large distribution centers have introduced a hybrid “parts-to-picker” order picking system consisting of a pallet warehouse and a tote warehouse to meet diverse order requirements. This system enables collaborative operations between two warehouses during picking and centralized replenishment processes. Additionally, it innovatively allows surplus goods remaining on pallets after picking to be replenished into the tote warehouse. Therefore, making informed decisions during operations such as picking, centralized replenishment, and picking replenishment will significantly enhance overall warehouse operational efficiency. However, to the best of our knowledge, such research has not yet been conducted. This paper addresses the comprehensive decision-making problem of replenishment and picking in a hybrid “parts-to-picker” order picking system. To solve it, we propose an intelligent decision-making framework based on deep reinforcement learning (DQN). We design a state space that incorporates predictive orders, composite warehouse inventory, and warehouse unit status. Furthermore, we design an action space that includes centralized replenishment, picking replenishment, and picking actions. This approach ultimately achieves three objectives: the allocation of quantities for pallet and tote picking, the allocation of quantities for centralized replenishment, and decisions on whether to replenish after picking. The DQN model also combines a reward function that includes penalty factors with an $$-greedy decay strategy, effectively improving the goal of order processing efficiency. The experimental results show that, compared with traditional scheduling strategies and intelligent algorithms, the decision-making model trained by the DQN architecture proposed in this paper can respond quickly in the comprehensive decision-making of picking and replenishment in a hybrid “parts-to-picker” order picking system, and can significantly improve system efficiency. The DQN model improves efficiency by approximately 44% and 17% compared to empirical decision-making and meta-heuristic algorithms, respectively. This study provides a solution that combines theoretical innovation and engineering feasibility for the multi-functional collaborative optimization of smart warehouse logistics systems, demonstrating significant practical value.