The rapid expansion ofMobile Edge Computing(MEC) enables latency-sensitive applications by extending computation to the network periphery. Yet its distributed and dynamic nature poses significant challenges for secure key management and consistent trust governance. ConventionalPublic Key Infrastructure(PKI) schemes suffer from heavy cross-domain overhead and fragile certificate maintenance, revealing a fundamental mismatch between centralized trust management and decentralized operation. To overcome these limitations, we present PRISM (Policy-Regulated Identity-Based Secure Messaging), a decentralized security framework designed for dynamic MEC ecosystems. PRISM integratesIdentity-Based Encryption(IBE) andAttribute-Based Access Control(ABAC) to realize unified authentication and fine-grained authorization. In parallel, blockchain-based smart contracts enforce network-wide policies and ensure global integrity. A hierarchical design supports scalable coordination and adaptive key lifecycle management, while a two-tier on-chain governance layer maintains transparency and state consistency. Formal analysis and experiments show that PRISM effectively constrains on-chain storage overhead, achieves a 100% failure detection rate when the reporting window exceeds six rounds, and maintains a stable topology (Adjusted Rand Index, ARI > 0.8) under intensive churn. These results demonstrate that PRISM effectively reconciles decentralized control with secure coordination, offering a practical foundation for consistent trust governance in large-scale MEC environments.

In recent years, edge networks have been deployed broadly at large scale, hosting a wide variety of services. Among these, microservices have emerged as one of the predominant service paradigms. Typically, microservices run on edge servers with varying configurations, while new microservice instances are usually online generated and join in edge networks due to the dynamic attributes of requests and networks. In those cases, an effective microservice online deployment solution are expected to be vital to system performance. So it becomes a critical issue to design online deployment solutions for microservices in edge. Existing research has always focused on offline deployment of microservices. However, edge networks are characterized by dynamics, real time, and concurrency, and when the environments or requests change, traditional offline deployment solutions usually cannot handle the deployment task in such cases. To address these issues, we carry out a comprehensive investigation on those potential influencing factors in edge, fully covering deployment cost, load balance, packet loss, and network delay. We further develop an innovative holistic online deployment solution that encompasses a system model, constraint analysis, multiobjective optimization, and a deep reinforcement learning algorithm. We conducted extensive experiments and evaluated our solution over a set of metrics using a real-world microservice prototype system. The results show that our online deployment solution produces superior performance, for example, reducing deployment cost by an average of 70.14% compared to all baselines. We also evaluated our solution under varying volumes of requests and gave analysis for performance stability and parameter sensitivity. We have released the code on GitHub.

In the field of Web knowledge base mining and Web services, the recognition service for named entity faces many challenges such as context complexity, semantic subtlety, and fuzzy entity boundaries, all of which require highly accurate and robust recognition service. The current services usually fail to reach those conditions. To address this issue, this paper proposes a recognition service for named entities for large Web knowledge bases, and the core contrition is the developed dual multilayer feature learning (D-MLFL) service, which combines projected gradient descent (PGD), adversarial learning, and a fused attention mechanism. Our service successfully addresses the challenges of complex context and subtle semantics faced by named entity recognition tasks. Our service integrates deep language models, recurrent neural networks, and conditional random fields, and clearly outperforms existing approaches in many sub-tasks, including in feature extraction, sequence modeling, and label decoding, especially in dealing with complex and diverse entity types and contextual relationships. We performed sufficient experiments, and the results show that our service significantly enhances the robustness against noise and abnormal Web data. The ability to extract entity features is improved, resulting in higher accuracy in identifying entities with fuzzy boundaries and complex semantics.

In representation learning for Web temporal knowledge bases, each node in Web knowledge bases carries a specific contextual meaning. Existing services often neglect the implicit semantic Web knowledge behind entities and relations, thus failing to effectively capture the knowledge representation of temporal Web knowledge bases. To address this issue, this paper develops an autocompletion service for temporal Web knowledge bases, which is based on multisource semantic feature learning and feature fusion. We construct a semantic model oriented toward external semantic Web repositories to supplement entity-relation descriptions, and our service leverages the pretrained language model BERT, effectively learning semantic knowledge features. Additionally, our service captures the textual features of quadruples using a recurrent neural network, constructs a historical sparse timestamp matrix, and generates a mask tensor, successfully obtaining the weights of potentially correct entities, and thereby capturing the historical features of quadruples. Furthermore, our service integrates complementary features from different modules through an attention mechanism. Experimental validation shows that our service outperforms existing approaches in terms of four evaluation metrics: mean reciprocal rank (MRR), Hits@1, Hits@3, and Hits@10. The results also exhibit that it improves the accuracy and performance for autocompletion service for temporal Web knowledge bases.

Smart contracts, due to their immutability and transparency upon deployment, entail significant economic and systemic risks from any vulnerabilities present. Traditional vulnerability detection methods suffer from low automation and high false positive rates, while existing deep learning-based approaches inadequately extract contract features, thereby limiting detection accuracy. To address these issues, this paper proposes FEMD: a feature-enhanced aided multimodal feature fusion method for smart contract vulnerability detection. Building on the foundation of addressing the low automation of traditional detection tools, our method improves the model’s feature extraction performance and detection capabilities. Specifically, we construct a contract graph through Comprehensive Expert-Graph Fusion, combining multi-modal feature fusion using a multi-head attention mechanism with expert patterns to ensure the capture and effective preservation of all critical information during the fusion process. To delve deeper into potential information within smart contract graphs, we employ a Feature Enhancer that leverages transpose operations on feature matrices to extract complex interaction patterns across different dimensions. Our approach is validated through batches of experiments on the Ethereum open dataset focusing on reentrancy and timestamp dependency vulnerabilities, demonstrating significant improvements in detection accuracy and robustness.

With the advancement of cloud computing technology, cloud-based personal health record (CB-PHR) has become an increasingly popular way for modern patients to flexibly manage and share their health records with doctors. However, the confidentiality of CB-PHR privacy is vulnerable to threats due to unauthorized users and untrusted cloud service provider (CSP). Additionally, patients and doctors may be constrained by changes in access permissions and limited device resources. To address these challenges, we propose an efficient decentralized privacy-preserving attribute-based access control scheme with verifiable policy update (DPVPU) for CB-PHR systems. DPVPU supports large attribute universe and safeguards the privacy of both the access policy and the doctor’s identity through partially hiding the access policy and employing a one-way anonymous key agreement technique. Unlike re-encrypting ciphertext, it can dynamically update policy by fully utilizing the previous policy and outsourcing the computation of ciphertext update to the CSP. Also, we design an efficient verification algorithm enabling patients to check the correctness of updated ciphertext. For devices with limited resources, we use online/offline and outsourced decryption techniques to reduce system costs. Finally, we provide formal security proofs and performance analysis to demonstrate the security and practicality of DPVPU.

The rapid increase of the data of user behaviors on the Internet brings a promising chance to better discover user preferences. Recommender systems have become a popular tool for the discovery of user preferences. One key issue is how to employ user behavior sequences to develop effective sequential recommendations, especially when behavior sequences are biased. The current sequential recommendation methods either can only mine data dependencies but ignores bias or only can learn bias but cannot mine data dependencies. To solve these problems, in this article, we propose a neural collaborative sequential learning mechanism, which learns sequential information from user behavior sequences that contain bias. We propose a neural collaborative filtering (NCF) model that fully takes advantage of all data dependencies among users, items, and biased sequential behaviors. Our sequential learning mechanism employs a self-attention mechanism to learn sequential features into an embedding space and inputs this sequential embedding into the generalized matrix factorization (GMF) model and the multilayer perceptron (MLP) model. We performed experiments on two real-world datasets and compared our model with many well-known baselines. The experimental results demonstrate that our model achieves superior performance. We also give a thorough analysis through ablation experiments and sensitivity experiments.

Nowadays, the emergence of online trading greatly facilitates people’s life. Meanwhile, online trading also brings hidden dangers, such as online fraudulent trading. To solve the issue, researchers have proposed many different detection models. However, in actual business scenarios, fraudulent transactions usually only account for a small portion of normal transactions, resulting in extremely imbalanced data. Besides, the concealment of fraud is reflected in that the fraudsters are imitating the normal transactions of users, posing a huge challenge for fraudulent transaction detection modeling. Inspired by generative adversarial networks (GANs), we propose a GAN-based framework to detect online banking fraud on extremely imbalanced data, called BalanceGAN. A fraud detection model is first pretrained using the data generated by the generator and then the model is fine-tuned using transfer learning on real-world datasets, by using this approach to address data imbalances. Compared with the conventional methods for solving imbalanced data, our BalanceGAN can avoid over-fitting of the model relatively, experiments on two real datasets show that our BalanceGAN has more than 10% performance improvement in Precision and Recall.

Nowadays an increasing number of software developers have joined in open-source software development communities such as GitHub, and develop and share softwares in these communities. Those online communities contain a huge volume of open-source repositories. Developers commonly search from existing repositories and intend to find suitable repositories to their development requirements. However, it is time-and energy-consuming to discover suitable repositories from such a large number of candidates and it may be also hard for developers to choose accurate keywords. So an effective repository recommendation service becomes an indispensable tool for developers. There have been some solutions for repository recommendation, but existing solutions have several defects such as mediocre accuracy and ignorance of useful features. In this paper, we develop a new personalized repository recommendation service with multi-modal features learning. We propose to mine two modes of features and jointly utilize the mined multimodal features. One of the features is the developers’ sequential behavior features and the other is text features of repositories. We design novel features learning mechanisms for the two modes of features. We performed sufficient experiments on a real-world dataset and the experimental results demonstrate that our model generates superior recommendation results and produces an improvement of 15.3% and 14.5% in Precision and Recall compared to well-known existing methods.

Abstract Malware detection is an important task for the ecosystem of mobile applications (APPs), especially for the Android ecosystem, and is vital to guarantee the user experience of Android APPs. There have been some exiting methods trying to solve the problem of malware detection, but the methods suffer from several defects, such as high time complexity and mediocre accuracy, which seriously decrease the practicability of existing methods. To solve these problems, in this study, we propose a novel Android malware detection framework, where we contribute an efficient Application Programming Interface (API) call sequences extraction algorithm and an investigation of different types of classifiers. In API call sequences extraction, we propose an algorithm for transforming the function call graph from a multigraph into a directed simple graph, which successfully avoids the unnecessary repetitive path searching. We also propose a pruning search, which further reduces the number of paths to be searched. Our algorithm greatly reduces the time complexity. We generate the transition matrix as classification features and investigate three types of machine learning classifiers to complete the malware detection task. The experiments are performed on real‐world Android Packages (APKs), and the results demonstrate that our method significantly reduces the running time and produces high detection accuracy.

Abstract Due to its development agility, continuous delivery, scalability and other characteristics, the microservice architecture systems (MASs) have provided complex business functions to hundreds of millions of users in many application fields. The operation and maintenance governance for a large number of microservices with complex relationships is crucial to ensuring the stability and reliability of an MAS. Although this research field has received certain attention and produced some innovative results, there is a lack of systematic reviews covering the different aspects of it. In this context, the central objective of this study is to carry out a systematic literature review (SLR) in this field, in an attempt to review existing issues, discuss the main trends, and share the findings with the academia. As a result, we start from more than 500 scientific papers published from 2009 to 2021 and extract 144 most significant papers, identify that the main research directions of this field include load balancing, fault detection, and autoscaling. Subsequently, we provide a comprehensive description of these research directions, discuss them in particular detail. We also determine limitations of current work and discuss new directions worth exploring in the future. Consequently, the outcomes will assist professionals and experts in the industry as well as academic researchers to focus more on operation and maintenance governance of MASs and further improve the relevant methods and theoretical systems in this field.

Gang fraud, the major and primary security issue in online lending services, can be efficiently solved by the data-driven paradigm that is recognized as a promising solution for online lending gang fraud prediction. However, it is challenging that such predictions need to detect evolving and increasingly impalpable fraud patterns based on low-quality data, i.e., very preliminary and coarse applicant information. The technical difficulty mainly stems from two factors: the extremedeficiency of information associationsandweakness of data labels. In this work, we mainly address the challenges by enhancing the utility of associations (i.e.,recovering missing associationsandmining underlying associations) on a knowledge graph. Specifically, we first propose an efficient method of Chinese address disambiguation to recover some critical associations that are broken by the ambiguity of applicant information, e.g., address related information. Then, to mine the implicit associations, we design a novel association representation method, calledAdaptive Connected Component Embedding Simplification Scheme(ACCESS), which can adaptively implement embedding for different connected components depending on their sizes. Finally, we adopt the graph clustering algorithms and devised predicting schemes based on the above enhanced associations to predict gang fraud in the case of weakness of data labels. Moreover, we propose a framework called RMCP by integrating the above techniques, which is consists of four steps:Recovering,Mining,Clustering, andPredicting, for efficiently predicting gang fraud. The good performance is validated by the experiments on a real-world dataset from a commercial lending company. Meanwhile, we provide a visual decision support system namedLongArmsover the RMCP framework.

Sentiment analysis is an important task in social computing and behavior analysis, and is a typical indicator of social health. It is a challenging mission to predict the sentiment of people in socially implemented Internet of Medical Things (IoMT) systems. The existing methods have several defects, and a typical defect is that most methods ignore the fact that there is much noise in IoMT systems and it is far not enough only to develop classification models for one type of data. In socially implemented IoMT systems, many methods treat the review text as plain text but ignore the potential knowledge structure. To solve those problems, in this article, we propose a novel solution, which is composed of adversarial learning and a hierarchical attention mechanism. We construct a hierarchical attention mechanism to learn the knowledge structure of a text. We propose to apply the attention mechanism both at the word level and sentence level, enabling us to learn the knowledge from each word and each sentence. We propose to use adversarial learning to learn new knowledge as non-random perturbations, which promotes the model’s robustness. We evaluate our method on several large-scale real-world datasets, covering a wide range of cases of sentiment analysis. Experimental results demonstrate that our method achieves superior performance compared to state-of-the-art methods.

A fundamental issue of the vehicular digital twin (DT) is efficiently synchronizing the data between the DT and the vehicular user (VUE). In this paper, we consider the heterogeneous vehicular networks (HetVNets) in which a VUE can connect to the network through different networks. The HetVNets can improve the efficiency of communication by providing seamless connections. However, the uneven distribution of VUEs and the dynamics of HetVNets make the environment more complex. Therefore, we propose the network selection algorithm for data synchronization between VUEs and DTs in the HetVNets, where the behaviour between the VUEs is considered as a competition for wireless resources. A learning-based prediction model residing in the DT is developed where the DT can predict the waiting time of each relay and transmit the predicted results to the VUE for decision-making. We model the network selection problem as a potential game considering both the transmission time and the waiting time obtained from the prediction model and prove the existence of Nash equilibrium (NE). We analyze the performance of the proposed algorithm, and simulation results show that our approach can effectively find the optimal strategy while achieving a fast convergence speed and high-level performance compared to the baselines.

This paper considers the downlink data synchronization from the digital twin (DT) to the vehicle, in which a vehicle drives through consecutive roadside units (RSUs) along its trip, and the DT on the cloud transmits the data to the vehicle through the relay of RSUs. To this goal, the DT first chops the data into blocks and cache them in the RSUs along the driving path of the vehicle. The vehicle can then retrieve the blocks when driving into the RSU's coverage to recover the data. Since RSUs have different cache capacities and communication costs, the DT needs to determine how to optimally distribute the data blocks at RSUs so that vehicles can finish downloading all the data before the deadline yet with the minimal cost. To determine the optimal delivery strategy of DT, we model the problem as an optimization framework subject to the time-varying wireless channel of RSUs, their service load and the communication cost. We then resort to the Lyapunov optimization to derive a distributed solution. Using extensive simulation results, we demonstrate that our scheme can effectively reduce the cost of data synchronization and improve the network load performance.

With astonishing speed, bandwidth, and scale, mobile-edge computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery. Yet, along with the massive deployment of MEC servers, the ensuing energy issue is now on an increasingly urgent agenda. In the current context, the large-scale deployment of renewable-energy-supplied MEC servers is perhaps the most promising solution for the incoming energy issue. Nonetheless, as a result of the intermittent nature of their power sources, these special design MEC servers must be more cautious about their energy usage, in a bid to maintain their service sustainability as well as service standard. Targeting optimization on a single-server MEC scenario, we, in this article, propose neural network-based adaptive frequency adjustment (NAFA), an adaptive processor frequency adjustment solution, to enable an effective plan of the server’s energy usage. By learning from the historical data revealing request arrival and energy harvest pattern, the deep reinforcement learning-based solution is capable of making intelligent schedules on the server’s processor frequency, so as to strike a good balance between service sustainability and service quality. The superior performance of NAFA is substantiated by real-data-based experiments, wherein NAFA demonstrates up to 20% increase in the average request acceptance ratio and up to 50% reduction in average request processing time.

The research on Wi-Fi sensing has been thriving over the past decade but the process has not been smooth. Three barriers always hamper the research: 1) unknown baseband design and its influence; 2) inadequate hardware; and 3) the lack of versatile and flexible measurement software. This article tries to eliminate these barriers through the following work.First, we present an in-depth study of the baseband design of the Qualcomm Atheros AR9300 (QCA9300) NIC. We identify a missing item of the existing channel state information (CSI) model, namely, the CSI distortion, and identify the baseband filter as its origin. We also propose a distortion removal method.Second, we reintroduce both the QCA9300 and software-defined radio (SDR) as powerful hardware for research. For the QCA9300, we unlock the arbitrary tuning of both the carrier frequency and bandwidth. For SDR, we develop a high-performance software implementation of the 802.11a/g/n/ac/ax baseband, allowing users to fully control the baseband and access the complete physical-layer information.Third, we release the PicoScenes software, which supports concurrent CSI measurement from multiple QCA9300, Intel Wireless Link (IWL5300), and SDR hardware. PicoScenes features rich low-level controls, packet injection, and software baseband implementation. It also allows users to develop their own measurement plugins.Finally, we report state-of-the-art results in the extensive evaluations of the PicoScenes system, such as the >2-GHz available spectrum on the QCA9300, concurrent CSI measurement, and up to 40 and 1 kHz CSI measurement rates achieved by the QCA9300 and SDR. PicoScenes is available athttps://ps.zpj.io.

Abstract Sentiment classification is a key task in sentiment analysis, reviews mining, and other text mining applications. Various models have been proposed to build sentiment classifiers, but the classification performances of some existing methods are not good enough. Meanwhile, as a subproblem of sentiment classification, positive and unlabeled learning (PU learning) problem widely exists in real‐world cases, but it has not been given enough attention. In this article, we aim to solve the two problems in one framework. We first build a model for traditional sentiment classification based on adversarial learning, attention mechanism, and long short‐term memory (LSTM) network. We further propose an enhanced adversarial learning method to tackle PU learning problem. We conducted extensive experiments in three real‐world datasets. The experimental results demonstrate that our models outperform the compared methods in both traditional sentiment classification problem and PU learning problem. Furthermore, we study the effect of our models on word embedding. Finally, we report and discuss the sensitivity of our models to parameters.

With the prevalent application of Internet of Things (IoT) in real world, services have become a widely used means of providing configurable resources. As the number of services is large and is also increasing fast, it is an inevitable mission to determine the suitability of a service to a user. Two typical tasks are needed, which are service recommendation and service selection. The prediction for Quality of Service (QoS) is an important way to accomplish the two tasks, and there have been a series of methods proposed to predict QoS values. However, few methods have been used to study the QoS prediction in IoT environments, where contextual information is vital. In this article, we develop a holistic framework to attack the QoS prediction in the IoT environment, which is based on neural collaborative filtering (NCF) and fuzzy clustering. We design a fuzzy clustering algorithm that is capable of clustering contextual information and then propose a new combined similarity computation method. Next, a new NCF model is designed that can leverage local and global features. Sufficient experiments are implemented on two real-world data sets, and the experimental results verify the effectiveness of the proposed framework.

Abstract In multi‐authority attribute‐based encryption (ABE) systems, each authority manages a different attribute universe and issues the private keys to users. However, the previous multi‐authority ABE schemes are subject to such restrictions during initializing the systems: either the attribute universe is polynomially sized and the attributes have to be enumerated or the attribute universe can be exponentially large, but the size of the set of attributes, which will be used in encryption, is not more than a predefined fixed value. These restrictions prevent multi‐authority ABE schemes from being deployed in dynamic practice applications. In this paper, we present a large universe decentralized key‐policy ABE scheme without such additional limitation. In our scheme, there is no requirement of any central authority. Each attribute authority executes independently from the others and can join or depart the system allodiality. Our system supports any monotone access policy. The proposed scheme is constructed on prime order groups and proved selectively secure in the standard model. To the best of our knowledge, our scheme is the first large universe decentralized key‐policy ABE system in the standard model. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Multi‐authority attribute‐based encryption (ABE) is a generation of ABE where the descriptive attributes are managed by different authorities. In current multi‐authority ABE schemes, the scale of attribute universe employed in encryption is restricted by various predefined thresholds. In this paper, we propose an unbounded multi‐authority ciphertext‐policy ABE system without such restriction. Our scheme consists of multiple attribute authorities (AAs), one central authority (CA), and users labeled by the set of attributes. Each AA governs a different universe of attributes and operates separately. Moreover, there is no cooperation between the CA and AAs. To provide the private keys for a user, the AAs first issue partial attribute‐related keys according to the attributes; the CA then issues identity‐related keys and links these attribute‐keys with the user's global identifier. Both the identity‐related and the linked attribute‐related keys will be used in decryption. The proposed multi‐authority ciphertext‐policy ABE scheme can support arbitrary linear secret sharing scheme as the access policy. Performance analysis and security proof indicate that our scheme is efficient and secure. Copyright © 2015 John Wiley & Sons, Ltd.

Although fingerprint based localization is promising for indoor applications, its accuracy still remains a huge challenge. Most of existing approaches rely on the Radio Signal Strength (RSS) to generate fingerprints. However, merely using RSS is unable to accurately localize objects since such an one-dimensional fingerprint will be seriously influenced by the interference and multi-path effect in the indoor environment. In this paper, we propose a new localization approach based on multidimensional Wi-Fi fingerprint. Instead of only using RSS to construct fingerprint, we employ RSS, transmitted power, and channel information to construct an integrated fingerprint. The extended fingerprint enables fine-grained localization and tracking services. We also deign a cosine similarity based matching algorithm and enhanced particle filter mechanism to achieve accurate localization and tracking. Extensive experiment and implementation results show that the new fingerprint and proposed algorithms can achieve an accuracy within two meters in 90% of testing points, while demonstrating a good adaptability to complex indoor environments.

Radio Frequency Identification (RFID) techniques are widely used in many ubiquitous applications. The most important usage of RFID techniques is to read the tags within a reader's interrogation area such that the objects attached with those tags can be identified. In real practice, it is common that a tag is physically in the interrogation range, but cannot be read by the reader, due to the multipath effect and other interference. This phenomenon, namely the hidden tag problem, is a big challenge to achieve high identification rate. To address this problem, most prior works depend on empirical or measurement-based methods to tune the transmission power for readers. Such a case-by-case solution is impractical for generic implementation. In this paper, we theoretically and experimentally explore the reasons why hidden tag problem occurs. To alleviate its impact, we propose a unified and measurable model, PAL, to formulate this problem and its impact. Different from previous works, our solution is generic and fully compatible with existing EPC C1G2 protocol. The analysis and measurement based on our model can help to design and deploy RFID systems with high identification rate.

Cold chain logistics is of great importance for transporting temperature and vibration sensitive products. However, fine-grained surveillance remains challenging in cold chain logistics, due to the lack of multi-dimensional information that reflects the status of monitored objects. In this paper, we propose a multi-dimensional information sensing surveillance framework, named MISS, to timely detect abnormal events that occur in cold chain logistics. The sensed information, including temperature and acceleration etc., can be integrated to provide accurate detection on the abnormal events. By adopting minimum entropy and AVC algorithms, we can classify various status in cold chain logistics. We further perform real implementations and evaluations on a prototype, and examine the effectiveness of MISS.

Diagnosing a large-scale sensor network is a crucial but challenging task due to the spatiotemporally dynamic network behaviors of sensor nodes. In this demo, we present Sensor Anomaly Visualization Engine (SAVE), an integrated system that tackles the sensor network diagnosis problem using both visualization and anomaly detection analytics to guide the user quickly and accurately diagnose sensor network failures. Temporal expansion model, correlation graphs and dynamic projection views are proposed to effectively interpret the topological, correlational and dimensional sensor data dynamics and their anomalies. Through a real-world large-scale wireless sensor network deployment (GreenOrbs), we demonstrate that SAVE is able to help better locate the problem and further identify the root cause of major sensor network failures.