IEEE Transactions on Emerging Topics in Computing最新文献

{"title":"Interpretable Federated Learning for Unsupervised Video Anomaly Detection in Smart City","authors":"Wen-Dong Jiang;Chih-Yung Chang;Yu-Ting Chin;Tzu-Chia Huang;Diptendu Sinha Roy","doi":"10.1109/TETC.2026.3716873","DOIUrl":"https://doi.org/10.1109/TETC.2026.3716873","url":null,"abstract":"IoT-based smart-city surveillance requires video anomaly detection (VAD) systems that can scale to massive unlabeled video streams, preserve data privacy, and operate efficiently on resource-constrained edge devices. However, existing federated-learning (FL) based VAD solutions still largely depend on costly annotations, incur high computation and communication overhead, and provide limited interpretability. To address these deployment barriers, we propose IFL-VAD-DSA, an interpretable, lightweight, and unsupervised FL framework for video anomaly detection in smart-city surveillance. First, we introduce a coarse-to-fine pseudo-labeling pipeline to eliminate the need for manual labels: a geometric modeling stage separates normal and suspicious videos at the video level, followed by a dynamic-programming sliding-window localization stage that refines pseudo-labels for precise temporal anomaly detection. Second, we design intermittent learning with variance-aware dynamic scheduling and lightweight model optimization to reduce synchronization frequency, computational burden, and communication cost, enabling efficient training on low-power IoT edge platforms. Third, we develop a transparent aggregation mechanism based on decoupled Shapley-value contribution analysis, which quantifies both individual client importance and interaction effects to improve fairness and interpretability in multi-source federated collaboration. Extensive experiments on UCF-Crime and XD-Violence demonstrate that IFL-VAD-DSA achieves state-of-the-art performance among the compared unsupervised and collaborative VAD methods, while also outperforming several weakly supervised and centralized baselines. Specifically, it reaches 88.31% and 89.64% AUC on UCF-Crime and XD-Violence in benchmark comparisons, and achieves 82.35% and 84.66% AUC under the unsupervised collaborative FL setting. These results confirm that IFL-VAD-DSA provides strong detection accuracy, improved resource efficiency, and actionable explanations of participant contributions for trustworthy smart-city surveillance.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1164-1180"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"adF: A Novel System for Measuring Web Fingerprinting Through Ads","authors":"Miguel A Bermejo-Agueda;Patricia Callejo;Rubén Cuevas;Ángel Cuevas;Álvaro Mayol","doi":"10.1109/TETC.2025.3630046","DOIUrl":"https://doi.org/10.1109/TETC.2025.3630046","url":null,"abstract":"This paper introduces <italic>adF</i>, a novel system for analyzing the vulnerability of different devices, operating systems, and browsers to web fingerprinting. <italic>adF</i> performs its measurements from code inserted in ads. We use our system in several ad campaigns that delivered 5.40 million ad impressions. The collected data allow us to assess the vulnerability of current desktop and mobile devices to web fingerprinting. We estimate that 66% of desktop and 40% of mobile devices can be uniquely fingerprinted with our web fingerprinting system. However, web fingerprinting resilience varies significantly across browsers and device types, with Chrome on desktops being the most vulnerable configuration. To counter web fingerprinting, we propose <italic>ShieldF</i>, a straightforward solution that blocks browsers from reporting attributes with the most significant discrimination power, as identified through our dataset analysis. Our experiments reveal that <italic>ShieldF</i> outperforms all anti-fingerprinting solutions proposed by major browsers (Chrome, Safari and Firefox), providing a resilience boost of up to 62% for some device configurations. <italic>ShieldF</i> is accessible as an add-on for any Chromium-based browser. Moreover, it is readily adoptable by browser and mobile app developers. Its widespread use would lead to a marked enhancement in browsers’ and mobile apps’ defense against web fingerprinting.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"852-866"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling Obfuscation Detection in Binary Software Through eXplainable AI","authors":"Claudia Greco;Michele Ianni;Antonella Guzzo;Giancarlo Fortino","doi":"10.1109/TETC.2024.3439884","DOIUrl":"10.1109/TETC.2024.3439884","url":null,"abstract":"Binary obfuscation techniques are commonly employed to protect code against reverse engineering and piracy. Unfortunately, besides being used for legitimate purposes, virus writers also resort to obfuscation to evade antivirus detection mechanisms based on signature scanning. Consequently, the detection of obfuscated code in executables may be a precious resource to prevent the execution of malicious programs. Detecting obfuscation is a task fraught with difficulties owing to the wide range of possible obfuscation transformations and the indistinguishability of obfuscated code. In this article, we venture into the not-so-explored world of obfuscation detection, gaining a deeper comprehension of what happens - from a statistical perspective - to a binary program when obfuscation transformations are applied to it. We accomplish this goal by leveraging eXplainable Artificial Intelligence, which allows us to discern the altered features from the invariant ones, which in turn can then be used for obfuscation-resilient malware detection. The present study has been carried out utilizing diverse datasets, not only to examine the detection of obfuscation but also to classify the specific obfuscating transformations employed. The investigation encompasses binaries compiled for various architectures, and we propose an effective methodology for identifying both the existence of obfuscation and isolating invariant patterns that can facilitate the creation of obfuscation-resistant signatures for antivirus detection.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"765-776"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216775","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":"Efficient Revocable Multi-Authority Attribute-Based Signcryption for Edge Server-Assisted IoT","authors":"Chen Zhu;Yinxia Sun;Yang Lu;Nian Xia;Xingrui Wei","doi":"10.1109/TETC.2026.3719257","DOIUrl":"https://doi.org/10.1109/TETC.2026.3719257","url":null,"abstract":"Internet of Things (IoT) technology has revolutionised data management. Cloud-assisted IoT solves the problem of limited storage and computing power of IoT devices, but data security issues, such as confidentiality, and authenticity remain a huge challenge. Multi-authority attribute-based signcryption (MA-ABSC) can effectively solve this problem and avoid the single-point bottleneck problem in attribute-based signcryption (ABSC) with a single authority. However, in existing MA-ABSC schemes the length of the user secret key and the ciphertext increases linearly with the number of authorities, which leads to more storage requirements for IoT devices. This paper proposes an efficient MA-ABSC scheme, in which both the length of the user secret key and the ciphertext are independent of the number of authorities. We also outsource part of the unsigncryption work to the edge servers to reduce the computation cost. In addition, our scheme enables efficient revocation of data users. Security analysis proves that MA-EABSC is indistinguishable under adaptive chosen ciphertext attacks, and existentially unforgeable under adaptive chosen message attacks. The experiments show that our scheme is very efficient and practical for edge server-assisted IoT.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1253-1266"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Communication Channel as a Physical Reservoir Computer","authors":"Mustafa Uzun;Kaan Burak Ikiz;Murat Kuscu","doi":"10.1109/TETC.2026.3708167","DOIUrl":"https://doi.org/10.1109/TETC.2026.3708167","url":null,"abstract":"Molecular Communication (MC) channels are characterized by significant memory and nonlinear dynamics arising from diffusion and receptor kinetics. While these properties are often treated as impairments to reliable data transmission, this work investigates whether they can instead be exploited as computational resources. We frame a canonical point-to-point MC channel, comprising ligand diffusion and reversible ligand–receptor binding at a spherical receiver, as a Physical Reservoir Computer (PRC). Using deterministic mean-field modeling and particle-based spatial stochastic simulations, we evaluate the MC system’s capability for temporal information processing on standard chaotic time-series benchmarks. Performance is characterized using both task-specific Normalized Root Mean Square Error (NRMSE) and the task-independent Information Processing Capacity (IPC). Our results reveal a non-monotonic dependence of computational capability on key biophysical parameters, including receptor kinetic rates, diffusion coefficient, and transmitter–receiver distance, identifying operating regimes where memory and nonlinearity are balanced. These findings demonstrate the potential of the MC channel as a biologically compatible computational substrate and provide initial design guidelines pointing toward MC networks that co-locate sensing, communication, and computation.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1108-1121"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"$mu$Sherlock: Continuous Intrusion Detection for Dynamic Microservice Applications","authors":"José Flora;Nuno Antunes","doi":"10.1109/TETC.2026.3682163","DOIUrl":"https://doi.org/10.1109/TETC.2026.3682163","url":null,"abstract":"The adoption of a microservice-based system provides many benefits but also exacerbates concerns about the dependability and security of the system. Traditional intrusion detection systems (IDS) often fail to adapt to the elasticity and rapid evolution of such applications, leading to model drift and reduced detection effectiveness. This paper presents <bold><inline-formula><tex-math>$mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic></alternatives></inline-formula>Sherlock</b>: a framework for continuous, autonomous, and adaptable intrusion detection for dynamic microservice applications. The framework leverages system call analysis and introduces the Model Reusability Index (MRI), a novel metric that triggers proactive retraining of detection models when they diverse from the application behavior. The framework is validated using a diverse and complex container-based testbed, to showcase the the scalability and adaptability capacities. Evaluation results demonstrate the performance, detection effectiveness, and the adaptation operations that were implemented into the framework.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"840-851"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive ELM-Based Time-Constrained Control of Uncertain Robotic Manipulators With Minimum Learning Computation","authors":"Xiaozheng Jin;Yuhan Hou;Xiaoming Wu;Jing Chi","doi":"10.1109/TETC.2025.3546241","DOIUrl":"https://doi.org/10.1109/TETC.2025.3546241","url":null,"abstract":"This article studies the extreme learning machine (ELM)-based time-constrained trajectory tracking control of a class of robotic manipulator systems with model/input uncertainties, matched and mismatched disturbances. A uniform robust exact differentiator (URED) is developed to construct a multi-layer virtual control framework of the robotic manipulator system. Under the control framework, adaptive ELM-based control strategies are proposed to suppress the influence of the uncertainties and disturbances layer-by-layer, combining with the minimum learning parameter (MLP) technique which is employed to reduce the calculation complexity of ELM parameters. The significance of this study is that general model/input uncertainties, matched and mismatched disturbances can be effectively compensated within a limited time, so that bounded trajectory tracking of the robotic manipulator can be achieved avoiding the complexity explosion issue. The time-constrained stability of the closed-loop robotic error systems is proved through Lyapunov stability theory. Finally, comparative simulations are employed to display the feasibility and superiority of the designed robust adaptive ELM-based control schemes of a robotic manipulator system.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"919-930"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Efficient Verifiable Core Subgraph Search in Blockchain Databases","authors":"Bo Yin;Gang Yang;Qianwen Xie;Jingjing Li","doi":"10.1109/TETC.2026.3722808","DOIUrl":"https://doi.org/10.1109/TETC.2026.3722808","url":null,"abstract":"Blockchain technology is increasingly adopted in data-intensive industries. As blockchain data grows rapidly, users with limited storage and computational resources often participate as light nodes, relying on third-party service providers to store the full blockchain data and offer query services. In such scenarios, where service providers may maliciously tamper with data, ensuring the integrity of query results becomes critical. Additionally, graphs are widely used to model relationships among real-world entities. Identifying cohesive subgraphs, such as core subgraphs, is a fundamental problem in graph analysis, and applications in blockchain include anti-money laundering and account-based sharding. However, to the best of our knowledge, no existing research addresses verifiable core subgraph queries in blockchain environments. Key challenges include <i>representing graph structures, validating subgraphs, and handling diverse graph types (such as one-dimensional, multi-dimensional, undirected, and directed graphs).</i> In this paper, we introduce the concept of the partial neighbor set to capture graph structures, which eliminates edge redundancy and enables efficient search for associated edges. Building on this, we propose novel authenticated data structures (ADSs) that support both one-dimensional <inline-formula><tex-math>$k$</tex-math></inline-formula>-core and multi-dimensional <inline-formula><tex-math>$mathbf {m}$</tex-math></inline-formula>-core queries in directed and undirected graphs. These ADSs utilize vertex coreness as a search key and integrate cryptographic set accumulators and Bloom filters to efficiently generate proofs of mismatched vertices and edges in the graph. We further improve search efficiency and proof aggregation by introducing batch search and verification. Experimental results demonstrate the efficiency and effectiveness of the proposed approaches, especially for large graph data.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1197-1212"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MC-DiffNet: Mask-Constrained and Cycle-Consistent Diffusion Network for Unpaired Ultrasound Image Enhancement","authors":"Nuo Chen;Yongquan Zhang;Guolin Chen;Jinhao Zhao;Changmiao Wang;Amirmehdi Yazdani;Hai Wang","doi":"10.1109/TETC.2026.3695050","DOIUrl":"https://doi.org/10.1109/TETC.2026.3695050","url":null,"abstract":"Ultrasound imaging is a valuable tool in clinical diagnostics due to its real-time feedback and non-invasive nature. Despite these advantages, it often suffers from issues like low contrast, speckle noise, and blurred anatomical boundaries, which can compromise the accuracy of diagnoses. Although diffusion models have proven effective for image restoration, their use in enhancing unpaired ultrasound images is limited. Another limitation is lacking of structural constraints and region-specific guidance. To address these challenges, we introduce <bold>MC-DiffNet</b>, a multi-task, diffusion-based enhancement framework designed specifically for clinical ultrasound images in unpaired settings. Furthermore, the use of lesion masks provides structural guidance that enables the model to focus on pathological characteristics in ultrasound images. The framework incorporates three principal modules: a Cycle-Consistency Path, a Mask-Constrained Module, and a Context-Aware Classification Path. The Cycle-Consistency Path ensures consistency in unpaired training through degradation-reconstruction consistency. The Mask-Constrained Module incorporates lesion-aware structural constraints into the Structural Similarity Index Measure (SSIM) loss, aiming to maintain anatomical accuracy. Meanwhile, the Context-Aware Classification Path guides the enhancement process using semantic-level features. Together, these modules enable MC-DiffNet to enhance diagnostically significant regions with precision while preserving anatomical integrity. Experiments conducted on public ultrasound datasets reveal that our method outperforms existing techniques in both peak signal-to-noise ratio and SSIM. Remarkably, when applied to real-world unpaired datasets, MC-DiffNet offers improved structural fidelity and visual clarity. Further evaluation suggests that the enhanced images generated by our framework are beneficial for subsequent segmentation tasks, underscoring the clinical relevance and robustness of our approach.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 2","pages":"678-690"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Belief Network-Based Activity of Daily Living Monitoring for Fall Risk Prediction in Elderly","authors":"Deepika Mohan;Peter Han Joo Chong;Jairo Gutierrez;Mirza Mansoor Baig;Hui Li","doi":"10.1109/TETC.2026.3686005","DOIUrl":"https://doi.org/10.1109/TETC.2026.3686005","url":null,"abstract":"Despite advancements in healthcare and emerging technologies, falls among older adults remain a significant health issue. Recent research has increasingly focused on developing advanced monitoring methods, predicting, and preventing falls in this population. Achieving high performance in fall prediction requires a clear understanding of relevant features such as gait patterns, balance metrics, muscle strength, and environmental factors. Identifying these key indicators and incorporating data from wearable sensors, medical histories, and demographic information can significantly enhance predictive accuracy. This study proposes an intelligent fall prediction model that anticipates future falls in older adults by continuously monitoring their Activities of Daily Living (ADLs) and detecting abnormalities. The model uses a Deep Belief Network (DBN) that incorporates contrastive divergence for pre-training, backpropagation for fine-tuning, and the Adam Optimizer to minimize loss. Evaluation of the proposed model shows it achieved an accuracy of 91.67%, specificity of 100%, and sensitivity of 90.00% when compared to the Ground Truth (GT) and existing fall prediction approaches. These results suggest that advanced deep learning techniques can effectively assist in early fall risk prediction, potentially reducing the likelihood and severity of falls among older adults.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 2","pages":"543-556"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}