Transactions on Emerging Telecommunications Technologies最新文献

The Internet of Things (IoT) has revolutionized how people involve with technological innovations. However, this development has also brought up significant security concerns. The increasing number of IoT attacks poses a serious risk to individuals and businesses equally. In response, this article introduces an ensemble feature engineering method for effective feature selection, based on a systematic behavioral analysis by means of artificial intelligence. This method identifies and highlights the most relevant features from IoT botnet dataset, facilitating accurate detection of both malicious and benign traffic. To detect IoT botnet attacks, the ensemble feature engineering method incorporates distinct approaches, including a genetic algorithm-based genetic approach, filter selection methods such as mutual information, LASSO regularization, and forward-backward search. A merger approach then combines these results, addressing redundancy and irrelevance. As well, a wrapper algorithm called recursive feature removal is applied to further refine the feature selection process. The effectiveness of the selected feature set is validated by means of deep learning algorithms (CNN, RNN, LSTM, and GRU) rooted in artificial intelligence, and applied to the IoT-Botnet 2020 dataset. Results demonstrate encouraging performance, with precision between 97.88% and 98.99%, recall scores between 99.10% and 99.95%, detection accuracy between 98.05% and 99.21%, and an F1-score ranging from 98.45% to 99.82%. Moreover, the ensemble feature engineering approach achieved precision of 98.26%, recall score of 99.68%, detection accuracy of 98.49%, F1-measure of 99.00%, an AUC-ROC of 82.37% and specificity of 98.38%. These outcomes highlight the method's robust performance in identifying both malicious and benign IoT botnet traffic.

The rapid proliferation of Internet of Things (IoT) devices has revolutionized wireless sensor networks (WSNs), enabling real-time monitoring across various applications. However, this growth introduces critical security challenges, including data breaches, time consumption, and memory overhead, which limit the efficiency and scalability of the existing encryption models. To address these issues, this paper proposes a novel DNA-enclosed Fully Homomorphic Encryption (HD-FHE) scheme integrated with improved elliptic curve cryptography (IECC). The proposed approach leverages dual-layer encryption by combining the strengths of deoxyribonucleic acid (DNA) computing and homomorphic encryption to secure data processing without decryption. The IECC further enhances key generation efficiency and reduces resource consumption. The experimental results demonstrate significant improvements in encryption (1.675 ms for 3 KB) and decryption (1.582 ms for 3 KB) times, alongside high throughput (2.275 ms for 7 KB), outperforming the existing models. These results highlight the robustness of the proposed method in minimizing vulnerabilities to Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) while ensuring scalability in dynamic IoT environments. This work provides a significant contribution to IoT-based WSN security by achieving a balance between performance and protection, paving the way for secure and efficient data transmission in next-generation networks.

Recent advancements in reconfigurable intelligent surface (RIS)-assisted cell-free systems have primarily focused on improving coverage and reducing network costs. However, much of the existing literature assumes perfect knowledge of channel state information (CSI), which poses significant challenges in practical implementations. This study investigates the channel estimation problem in RIS-assisted cell-free systems, highlighting two key observations: (1) a shared channel exists between the base station (BS) and the RIS across all users, and (2) a similar common channel exists between the RIS and the users across all BSs. Building on these insights, the paper addresses the challenges of cascaded and two-timescale channel estimation. Specifically, two novel methods are introduced: (1) a 3D-MMV-based compressive sensing technique for efficient cascaded channel estimation, and (2) a pilot reduction strategy that leverages multi-BS cooperation to enhance channel estimation performance. These methods aim to improve the accuracy and efficiency of channel estimation in RIS-assisted cell-free systems while minimizing pilot overhead.

In this paper, we propose a 6-node system consisting of two transmitter-receiver pairs sharing the same spectrum, an eavesdropper, and a relay, all operating in a half-duplex. The eavesdropper is mainly interested in communication between one of the transmitter-receiver pairs, which we call the primary. Communication along with the second pair called the secondary, is performed in two hops/time slots with the aid of the relay. The main idea of our study is to investigate the secrecy performance of the primary pair when jamming is performed by the secondary relay-assisted path. In particular, in the first time slot, the secondary destination acts as a jammer relative to the eavesdropper by injecting artificial noise known to the primary pair. During the second time slot, the secondary transmitter acts as a jammer while the relay forwards data to the secondary destination. In effect, this allows for cooperation among the nodes both in transmitting primary and secondary data while reducing the eavesdropper's ability to listen in on the primary link communication. For the proposed protocol, we derive closed-form expressions of the intercept probability. We also obtain a closed-form expression of the outage probability along with the secondary communication link. Moreover, we study the effect of transmit power allocation on intercept and outage probabilities along with the different links. Theoretical and simulation results are given to prove that the proposed protocol can provide better security for the primary link and acquire acceptable secondary outage probability.

The rapid integration of Internet of Things (IoT) devices into healthcare systems has revolutionized medical care delivery but has also introduced significant security challenges. Ensuring secure communication, privacy preservation, and system resilience in resource-constrained healthcare IoT networks is critical, given the sensitivity of the data involved and the potential for malicious attacks. This research addresses these concerns by proposing a Multi-cluster Security Framework for Healthcare IoT, designed to overcome existing limitations in security and scalability. The framework combines Redundant Byzantine Fault Tolerance with Extensions (RB-BFT X) and CoatiNet, leveraging lightweight cryptographic techniques, role-based access control, and dynamic routing algorithms. RB-BFT X enhances intra-cluster security through fault tolerance and anomaly detection, while CoatiNet optimizes inter-cluster communication using adaptive routing and self-recovery mechanisms inspired by coatis' natural behavior. Experimental results demonstrate the framework's efficacy, achieving a high detection rate of 98.20%, minimal latency, and stable throughput under various adversarial conditions. Compared to existing methods, it outperforms in maintaining network lifetime and reducing false positives, even with increased malicious activity. These findings have significant implications for enhancing the security and efficiency of healthcare IoT networks. The proposed methodology ensures robust data protection, efficient communication, and adaptability to evolving threats, contributing to safer and more reliable healthcare systems.

Optical Wireless Communication (OWC) is a highly congruous communication system for the emerging Fifth Generation (5G) and Sixth Generation (6G) communication environments. The authors have discussed the efficacy of a terrestrial Free Space Optics (FSO) link in the coastal urban environments. Performance metrics such as received signal power and Link Margin (LM) are determined and hence used to judge the effectuation of the FSO model under consideration. Fog-Evoked Signal Degradation (FESD) is the indispensable contributor to atmospheric attenuation. Various models have been taken into account for the computation of FESD considering four consecutive months (November to February) for six consecutive years from 2018 to 2023. Mathematical analysis has been carried out from the real-time measured visibility data and wind speed values. Also, the altitude of the location has been considered for computing the scattering and Turbulence-Induced Attenuation (TIA). The LM is derived uniquely for summer and winter seasons to determine the feasibility for the establishment of the FSO link. For the purpose of the performance analysis, all three of the optical window wavelengths 850, 1300, and 1550 nm have been taken into account. Both the simulation results and mathematical estimates are used to compute the effective maximum achievable link range, link availability, and minimum visibility requirement for the specific geographic location under consideration.

Deepfake video detection is one of the new technologies to detect Deepfakes from video or images. Deepfake videos are majorly used for illegal actions like spreading wrong information and videos online. Hence, deepfake video detection techniques are used to detect videos as real. Several deepfake detection methods have been introduced to detect Deepfakes from videos, but some techniques have limitations and low accuracy in predicting the video as real or fake. This paper introduces advanced deepfake detection techniques, such as converting the video into frames, pre-processing the frames, and using feature extraction and classification techniques. Pre-processing of frames using the sequential adaptive bilateral wiener filtering (SABiW) removes the noise from frames and detects the face using the 2D Haar discrete wavelet transform (2D-Haar). Then, the features are extracted from a pre-processed image with a depthwise separable residual network (DSRes). Finally, the video is classified using the Convolutional attention advanced generative adversarial network (Con-GAN) model as a deepfake video or original video. The Mud ring optimization algorithm is used to detect the weight coefficients of the network. Then, the overall performance of the proposed model is compared with other existing models to describe their superiority. The proposed method uses four datasets, which are FaceForensics++, Celeb DF v2, WildDeepfake, and DFDC. The performance of the proposed model provides a high accuracy rate of 98.91% and a precision of 98.32%. The proposed model provides better performance and efficient detection by detecting Deepfakes.

In multi-user Massive Multiple Input Multiple Output (MIMO) systems, acquiring Channel State Information (CSI) at the transmission point is crucial for accurate estimation, but it fails by costs and complexity. The Massive MIMO networks are known for the improved Spectral Efficiency (SE). These systems are equipped with antenna groups at the receiving end and the transmission point. Analyzing Channel State Information (CSI) from faulty channels is maximizing the precoder's complexity. The complexity of determining the optimal disrupting vector improves power transmission but reduces SE. This makes the optimization process more challenging. Therefore, in this work, an Optimal Pilot-Based Vector Perturbation Precoding (OPVP) is introduced to improve the SE of the massive MIMO system. The Hybrid Flamingo Search-based Sparrow Search Optimization Algorithm (HFS-SSOA) is used to optimally select the perturbing vector for efficient reception as well as transmission and is developed by combining the Flamingo Search Algorithm (FSA) and Sparrow Search Algorithm (SSA). In addition, the ideal pilot designs wisely intellects the CSI for providing response to the transmitter. Further, the compressive sensing will be used by OPVP for effectively selecting the low-dimensional CSI. The suggested approach effectively detects the low dimensional CSI by considering the objective functions like computational complexity, transmitting power, and computational overhead which is used to develop the perturbing signal within the constellation bound. Finally, the simulation process is carried out on the developed model to prove its effectiveness.

With the rise of Industry 5.0, smart cities, and the ever-expanding use of general wireless networks, ensuring seamless communication and robust data security has become a critical challenge. Generating secure secret keys (SKG) through wireless channels is particularly complex in environments where noise and wideband conditions introduce discrepancies and autocorrelation in channel measurements. These issues compromise cross-correlation and randomness, leading to substantial bit disagreements, distinct keys at the transceivers, and unsuccessful SKG. This research begins by outlining the mathematical model of the signal preprocessing technique called multiscale principal component analysis (MSPCA). Subsequently, it explores the performance of key generation when employing the proposed scheme. A holistic system-level framework for creating initial shared keys is presented, encompassing quantization methods such as uniform multilevel quantization (UMQ) and encoding methods such as 3-bit Gray encoding. Monte Carlo-based simulations in an indoor scenario evaluate system efficacy using metrics like Pearson correlation coefficient, bit disagreement rate (BDR), randomness, and complexity. The proposed scheme achieves a BDR lower than 0.01, a correlation coefficient greater than 0.95, and passes all National Institute of Standards and Technology (NIST) randomness tests, establishing it as a viable solution for securing wireless systems. In the context of Industry 5.0 and smart city infrastructures, where seamless communication and robust data security are paramount, the proposed SKG framework offers significant potential. With its ability to ensure secure and reliable communication, this scheme can underpin the development of advanced wireless systems that cater to the high demands of interconnected ecosystems, enhancing resilience and trust in critical applications.

This study aims to methodically explore the diverse applications of blockchain technology (BT) within the healthcare domain. Additionally, it seeks to analyze the inherent challenges of integrating BT into healthcare systems. Furthermore, this study elucidates blockchain's advantageous contributions to the healthcare domain. The suggested research thoroughly reviews the literature from various databases, using predetermined criteria, such as exclusion and inclusion, to identify pertinent studies. It also demonstrates the properties of blockchain and its functionality for the patient, healthcare providers, or overall healthcare infrastructure to assist the healthcare industry in a contemporary direction. The substantial advantages of BT within the medical field are notable. However, it is vital to acknowledge the security vulnerabilities by employing a blockchain-centered strategy to mitigate these challenges, allowing for the creation of a robust and streamlined system and framework. The importance of this study is that as this investigation navigates the convergence of healthcare and BT, it not only delineates the multifaceted applications of BT but also meticulously examines the associated security challenges. The findings of this study chart a course toward a technologically advanced, secure, and efficient healthcare ecosystem, improving patient outcomes and reshaping the future of healthcare delivery worldwide.