Digital Communications and Networks最新文献

{"title":"TAR-PZKP: a secure transmission scheme for vehicle accident reports using PUF and zero-knowledge proof","authors":"Lizhe Liu,&nbsp;Weijie Tan,&nbsp;Shangyu Lv,&nbsp;Huan Zhuang,&nbsp;Yuling Chen,&nbsp;Chunguo Li","doi":"10.1016/j.dcan.2026.06.005","DOIUrl":"10.1016/j.dcan.2026.06.005","url":null,"abstract":"<div><div>In the Internet of Vehicles (IoV), the large-scale deployment of smart vehicles has triggered new road traffic safety challenges. Particularly, existing vehicle accident report transmission schemes still face challenges such as privacy leakage, Single Point of Failure (SPOF), physical cloning attacks, and excessive computational overhead. To address these issues, this paper proposes a secure accident report transmission scheme that uses Non-Interactive Zero-Knowledge Proof (NIZKP) and Physically Unclonable Functions (PUF). This paper designs a decentralized authentication scheme for vehicle registration that prevents SPOF and privacy leakage. We also use the PUF to realize two-factor authentication login, which effectively resists physical cloning attacks. In addition, the authentication process uses NIZKP based on the Pedersen commitment to realize authentication for accident report coordination. At the end of the accident report coordination, it is passed into the blockchain for storage, realizing the secure transmission of accident reports. To reduce the storage as well as computation overhead, this paper uses a key derivation function to update the key. Finally, formal security analysis was conducted using the Real or Random (ROR) model and the ProVerif tool, the results prove that the proposed protocol meets security requirements. Comparing our proposed scheme with related schemes, the computational overhead of our V2V scheme is reduced by 42.4%, with higher security and lower communication overhead.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 9","pages":"Pages 1367-1383"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149145280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing healthcare IoT: a meta-synthesis of challenges, solutions, and technology convergence","authors":"Mahmoud Zahedian Nezhad,&nbsp;Ali Javan Jafari Bojnordi,&nbsp;Seyed Amin Hosseini Seno,&nbsp;Javad Rezazadeh","doi":"10.1016/j.dcan.2026.07.004","DOIUrl":"10.1016/j.dcan.2026.07.004","url":null,"abstract":"<div><div>The Internet of Things (IoT) has transformed the healthcare industry, with technologies such as big data, blockchain, and artificial intelligence driving significant benefits through technology convergence. However, Healthcare IoT (H-IoT) systems face persistent security and privacy challenges. This meta-synthesis analyzes the current literature to identify key aspects of H-IoT security, including requirements, threats, and proposed technical solutions, while emphasizing the role of technology convergence in addressing these issues. The reviewed studies are categorized into two groups: qualitative research (focused on security features, requirements, threats, and attacks) and quantitative research (organized into six technical solution domains: blockchain; cryptography; authentication schemes and protocols; artificial intelligence and machine learning; H-IoT systems with embedded security solutions; and security and privacy frameworks). Finally, future research directions are explored to emphasize the critical role of technology convergence in enhancing H-IoT security.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 9","pages":"Pages 1396-1414"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149203283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A generalizable BERT-GNN hybrid framework for unified log anomaly detection","authors":"Caihong Wang,&nbsp;Du Xu,&nbsp;Yongsheng Zhao","doi":"10.1016/j.dcan.2026.04.001","DOIUrl":"10.1016/j.dcan.2026.04.001","url":null,"abstract":"<div><div>Logs are essential for monitoring large-scale distributed systems, yet their volume and structural heterogeneity pose substantial challenges for automated analysis. Conventional approaches often struggle to jointly model semantic information and temporal dependencies, which limits feature generalization across diverse environments. We propose Log2graphs, a generalizable framework for unified, unsupervised log analysis. Its core component, the Multi-Graph Autoencoder (MGAE), integrates Bidirectional Encoder Representations from Transformers (BERT) with a Graph Neural Network (GNN). Within MGAE, BERT extracts contextual semantic embeddings from raw log messages, while the GNN captures the chronological and topological correlations among log events. By reconstructing complex multi-graph structures, MGAE transforms sequential dependencies into low-dimensional representations without relying on labeled data. Experiments on widely used benchmark datasets, such as HDFS and BGL, show that the features extracted by MGAE improve downstream anomaly detection performance, outperforming baselines including LogFiT and LogFormer with F1 score gains of up to 11%. To extend benchmark validation to real-world settings with limited labeled datasets (e.g., HPC, Proxifier, and Zookeeper), we introduce a rigorous evaluation strategy using three domain-adaptive, label-free clustering metrics: Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. This strategy enables quantitative assessment of the learned feature space in unsupervised environments and reduces reliance on ground-truth logs.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 9","pages":"Pages 1355-1366"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149145281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond theory: a comprehensive survey of clustering algorithm applications in wireless and media communications","authors":"Chen Wang,&nbsp;Yu Li,&nbsp;Ning Xie","doi":"10.1016/j.dcan.2026.06.004","DOIUrl":"10.1016/j.dcan.2026.06.004","url":null,"abstract":"<div><div>The burgeoning advancement in communication technologies, coupled with the widespread adoption of mobile devices, has led to an exponential increase in communication data volumes. Navigating this deluge of data in the big data era poses significant challenges in terms of processing and analysis. Clustering, an unsupervised learning approach, stands out as a potent analytical tool in this context, capable of segmenting data into groups of similar entities to unearth underlying patterns and structures. Given its capacity to discern intrinsic data relationships, clustering has garnered considerable interest for its potential applications within the communication field. Despite the wealth of research, many existing surveys either focus solely on the theoretical aspects of clustering algorithms or restrict their scope to a single domain, often overlooking the intrinsic synergy between data transmission and content processing. Addressing this gap, this survey delivers an exhaustive review of clustering algorithm applications from a unique cross-disciplinary perspective, bridging the realms of wireless communications and media communications. We argue that as communication systems evolve towards semantic and content-aware networking, a unified view of these domains is essential. This comprehensive review not only aids readers in grasping the varied applications of clustering algorithms—ranging from physical layer security to semantic image analysis—but also empowers researchers to refine algorithm design by drawing on analogous use cases across these sectors. To enhance comprehension, we elucidate diverse clustering algorithm implementations within wireless and media communications, illustrating with detailed examples. Moreover, we highlight prevailing challenges and delineate future research trajectories for clustering algorithm applications in these fields.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 9","pages":"Pages 1325-1354"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149071896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anomaly detection in industrial control networks based on deep reinforcement learning and data reconstruction","authors":"Hanjun Gao,&nbsp;Jiaqi Sun,&nbsp;Wei Feng,&nbsp;Xiaoxuan Fan,&nbsp;Wei Xiang,&nbsp;Shenghao Liu,&nbsp;Xianjun Deng","doi":"10.1016/j.dcan.2026.07.010","DOIUrl":"10.1016/j.dcan.2026.07.010","url":null,"abstract":"<div><div>Any anomaly in the data flow of industrial control networks can directly translate into security vulnerabilities within the control system. Therefore, maintaining continuous situational awareness of industrial control networks and achieving precise identification and effective handling of abnormal data are critical to ensuring the reliability and functional safety of control systems. Anomaly detection tasks in industrial control networks aim to identify behavior deviating from normal patterns within network traffic or device data. Although existing unsupervised and self-supervised learning methods can train models without labeled data, their performance is limited by prior assumptions about data distributions, and it is difficult to ensure model stability when the training data is contaminated. To this end, this paper designs a Data Reconstruction and Anomaly Detection method, termed as DRAD. Specifically, the proposed method reconstructs the original data from the perspective of complete data-sample features, rather than relying solely on partial attribute learning. DRAD comprises four core modules. The state representation module maps industrial data stream samples to states of a Markov decision process. The action generation module outputs reconstructed data via a deterministic policy network. The state sampling module uniformly samples interactions from the simulated environment within the training set. The reward computation module designs instantaneous rewards based on reconstruction errors. Each module collaboratively optimizes within a deep deterministic policy gradient framework to maximize long-term cumulative rewards and learn normal data patterns. Moreover, DRAD gradually converges to a more robust detection strategy through long-term exploration, thereby significantly enhancing the stability of detection performance. Extensive experiments on eight datasets show that DRAD improves the AUC-ROC and AUC-PR by 6.8%-17.0% and 12.1%-47.3% and improves robustness under different anomaly contamination rates compared with seven representative competing methods.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 9","pages":"Pages 1461-1469"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149203286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-stage directed acyclic graph based privacy and incentive federated learning approach in industrial internet of things","authors":"Xing Liu,&nbsp;Yixuan Jiang,&nbsp;Haoyang Li,&nbsp;Yuzhao Hou,&nbsp;Xiaolong Zhou,&nbsp;Bin Cao","doi":"10.1016/j.dcan.2026.06.001","DOIUrl":"10.1016/j.dcan.2026.06.001","url":null,"abstract":"<div><div>To leverage industrial big data effectively, Federated Learning (FL) in Mobile Edge Computing (MEC) networks for Industrial Internet of Things (IIoT) control systems has been integrated to facilitate machine learning model development using local data on IIoT devices. To motivate IIoT devices to actively participate and collaborate, incentive mechanisms are typically incorporated. However, differential privacy mechanisms employed to protect local models in FL can degrade model accuracy and convergence by introducing noise into local parameters, an undesirable trade-off in incentive mechanism design for industrial applications. To address this, this paper introduces PI-FL, a differential privacy and incentive mechanism for FL based on Directed Acyclic Graphs (DAGs), designed to balance data privacy during training with motivation efficiency. PI-FL operates in two stages: In the first stage, industrial IoT devices are organized into districts overseen by MEC servers, which supervise global model development and implement incentive mechanisms to mitigate overuse of differential privacy. In the second stage, MEC servers from different districts upload global models to a DAG for collective sharing and validation. Extensive simulations assess PI-FL’s effectiveness, demonstrating that it significantly enhances FL model training accuracy while ensuring participant data privacy.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 9","pages":"Pages 1470-1480"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149203289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-enhanced UAV networks for post-disaster communication: a decentralized flocking approach","authors":"Sana Hafeez,&nbsp;Runze Cheng,&nbsp;Lina Mohjazi,&nbsp;Yao Sun,&nbsp;Muhammad Ali Imran","doi":"10.1016/j.dcan.2026.03.006","DOIUrl":"10.1016/j.dcan.2026.03.006","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) have significant potential for agile communication and relief coordination in post-disaster scenarios, especially when conventional ground infrastructure is compromised. However, effectively coordinating and securing swarms of heterogeneous UAVs from multiple service providers presents critical challenges related to privacy, scalability, lightweight consensus protocols, and cybersecurity resilience. This study proposes a blockchain-enabled UAV coordination framework that leverages consensus mechanisms, smart contracts, and cryptographic techniques to address these challenges. First, a consortium blockchain architecture is introduced, integrating Zero-Knowledge Proofs (ZKPs) to enable privacy-preserving, multi-agency coordination while ensuring access control and data security. Second, a hybrid Delegated Proof-of-Stake–Practical Byzantine Fault Tolerance (DPoS-PBFT) consensus protocol is developed to optimize security, efficiency, and resilience against node failures in resource-constrained UAV networks. Third, a decentralized flocking algorithm is proposed to enable adaptive and autonomous UAV cluster operations under dynamically changing connectivity conditions, ensuring seamless disaster relief functions. Comprehensive simulations show that the proposed system scales efficiently to 500 UAV nodes while maintaining high throughput and low latency, with only an 18 ms increase in latency from 10 to 500 nodes. The framework demonstrates strong cyber resilience, remaining robust under denial-of-service (DoS), spoofing, and tampering attacks. Furthermore, communication latencies remain under 10 milliseconds, with median values of approximately 2–3 ms, achieved through self-optimizing network intelligence. The results validate the proposed system as a secure, scalable, and high-performance solution for UAV-enabled disaster response, ensuring reliable emergency communication and efficient resource allocation in critical environments.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 9","pages":"Pages 1415-1429"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149203290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAF-IoMT: Secure authentication framework for IoMT-enabled smart healthcare system","authors":"Muhammad Tanveer,&nbsp;Saud Alhajaj Aldossari","doi":"10.1016/j.dcan.2026.04.005","DOIUrl":"10.1016/j.dcan.2026.04.005","url":null,"abstract":"<div><div>The Internet of Medical Things (IoMT) represents an advanced evolution of the Internet of Things (IoT), enabling secure and intelligent interactions among connected medical devices and significantly improving the efficiency and reliability of modern healthcare systems. In the IoMT environment, maintaining data similarity, integrity, and secure transmission is critical, particularly during emergencies such as the COVID-19 pandemic, when real-time, trustworthy medical data exchange becomes essential. Authentication protocols are indispensable for verifying the identities of communicating entities, preserving data integrity, preventing unauthorized access, and securing communication channels against malicious adversaries. However, many existing authentication schemes remain impractical for resource-constrained IoT medical devices due to high computational requirements and exposure to various security vulnerabilities. To address these challenges, we present SAF-IoMT, a mutual authentication protocol specifically designed for device-to-server communication within IoMT-enabled smart healthcare systems. SAF-IoMT integrates a PUF with the lightweight cryptographic primitive ASCON, thereby reducing operational overhead while maintaining strong security assurances. This design makes SAF-IoMT highly suitable for medical devices with limited processing capabilities and battery constraints. By enhancing security across the IoMT network, SAF-IoMT ensures reliable operation of medical devices, especially under critical healthcare conditions. The security of SAF-IoMT has been validated through both formal and informal security analyses. At the same time, performance evaluation demonstrates considerable reductions in computational cost (19.29% to 75.20%) and communication cost (11% to 66%) compared with existing related protocols.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 9","pages":"Pages 1481-1491"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149203288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Asymmetric digital backpropagation for long-haul digital subcarrier multiplexing fiber optical transmission","authors":"Zejun Chen,&nbsp;Sailan Yan,&nbsp;Chao Li,&nbsp;Jiahao Huo,&nbsp;Gai Zhou,&nbsp;Meng Xiang,&nbsp;Peng Sun,&nbsp;Zhixue He,&nbsp;Keping Long,&nbsp;Songnian Fu,&nbsp;Yuwen Qin","doi":"10.1016/j.dcan.2026.09.002","DOIUrl":"10.1016/j.dcan.2026.09.002","url":null,"abstract":"<div><div>Digital Subcarrier Multiplexing (DSM) has emerged as a preferred solution over its Single-Carrier (SC) counterpart for high-speed long-haul Standard Single Mode Fiber (SSMF) transmission, owing to its flexible spectral allocation and adaptive transmission capabilities. However, fiber nonlinearity remains a fundamental limitation on the transmission bandwidth-distance product, while Digital Backpropagation (DBP) serves as a representative Nonlinearity Compensation (NLC) method. Traditional symmetric DBP assumes a uniform nonlinearity accumulation over the SSMF link, which deviates from the inherently asymmetric power profile in practice. Specifically, we propose an asymmetric DSM-DBP scheme based on a nonlinear accumulation asymmetry metric Δ<em>E</em>(<em>ρ</em>), which enables the efficient determination of the optimal dispersion splitting ratio <em>ρ</em> ∈ [0, 1] without exhaustive optimization. Consequently, we can efficiently determine the position of nonlinear compensation operators within each DBP step for high-speed long-haul DSM transmission systems. When the single-wavelength, four-subcarrier, 800 Gb/s Probabilistic Shaping Dual-Polarization 64-Quadrature-Amplitude-Modulation (PS-DP-64QAM) signals are transmitted, the asymmetric DSM-DBP increases the optimal launch power to 5dBm and achieves a Signal-To-Noise Ratio (SNR) improvement of 0.2 dB over symmetric DSM-DBP after the 1600 km SSMF transmission. Under a Normalized Generalized Mutual Information (NGMI) threshold of 0.89, the SSMF reach can be extended to 2400 km, representing a reach extension of approximately 160km compared to traditional symmetric DSM-DBP. Meanwhile, the robustness of the proposed asymmetric DSM-DBP is numerically verified for various modulation formats, variable baud rates, and different subcarrier configurations. These results highlight the scalability and practical relevance of the proposed asymmetric DSM-DBP for next-generation high-capacity optical networks.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 9","pages":"Pages 1492-1500"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149203243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightweight adversarial membership inference attack with temporal evolution against federated learning","authors":"Tiansen Liang,&nbsp;Xiaodong Wang,&nbsp;Mingyang Yu,&nbsp;Shuai Yuan,&nbsp;Zhitao Guan","doi":"10.1016/j.dcan.2026.09.007","DOIUrl":"10.1016/j.dcan.2026.09.007","url":null,"abstract":"<div><div>Membership Inference Attacks (MIAs) pose a significant privacy threat to Federated Learning (FL) by enabling adversaries to exploit behavioral discrepancies between original and perturbed samples. However, most existing adversarial MIAs overlook the attack efficiency. To address this challenge, we propose ADVMIA, a novel adversarial attack framework that redefines the optimization perspective in this domain. ADVMIA is the first method to leverage the temporal evolution characteristics of FL models, offering a deeper understanding of the internal dynamics of adversarial sample generation. Concurrently, it introduces a threshold selection strategy that eliminates the need to train additional inference models, thereby reducing the high computational overhead associated with iterative adversarial optimization. We evaluate ADVMIA on two real-world datasets and compare its performance against five state-of-the-art MIAs. Experimental results demonstrate that ADVMIA achieves superior efficiency and heightened attack effectiveness. Furthermore, its robustness in practical FL scenarios is systematically validated through evaluations against two common defense mechanisms.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 9","pages":"Pages 1430-1441"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149203284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}