IEEE Transactions on Services Computing最新文献_第10页

KA$^{2}$2SE: Key-Aggregation Authorized Searchable Encryption Scheme for Data Sharing in Wireless Sensor Networks KA2SE：用于无线传感器网络数据共享的密钥聚合授权可搜索加密方案

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-04 DOI: 10.1109/TSC.2024.3491378

Haijiang Wang;Jianting Ning;Wei Wu;Chao Lin;Kai Zhang

{"title":"KA$^{2}$2SE: Key-Aggregation Authorized Searchable Encryption Scheme for Data Sharing in Wireless Sensor Networks","authors":"Haijiang Wang;Jianting Ning;Wei Wu;Chao Lin;Kai Zhang","doi":"10.1109/TSC.2024.3491378","DOIUrl":"10.1109/TSC.2024.3491378","url":null,"abstract":"As a promising technology, key-aggregation searchable encryption with constant computation overhead is especially suitable for sensor nodes with limited computation resources in wireless sensor networks. However, in most of the existing key-aggregation searchable encryption schemes, the authorized aggregation key is generated in a deterministic way. As a result, these schemes suffer from “Key Forge Attack” and “Trapdoor Forge Attack” that we proposed and hence fail to support the security property as they claimed (which is an important goal to be achieved in key-aggregation searchable encryption schemes). To fix these flaws, in this paper, we identify the security challenges related to key-aggregation searchable encryption and propose a lightweight key-aggregation authorized searchable encryption scheme based on attribute-based encryption, called KA<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>SE. It enables a data owner to share encrypted data with an authorized query user by issuing only a single authorized aggregation key, and the authorized query user only needs to submit a single trapdoor to the cloud server to perform keyword search. We formulate the security definitions for KA<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>SE and prove its security. Finally, empirical evaluations demonstrate that KA<inline-formula><tex-math>$^{2}$</tex-math></inline-formula> SE is computationally efficient in comparison with existing schemes.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"226-238"},"PeriodicalIF":5.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580363","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}

引用次数: 0

RESTLess: Enhancing State-of-the-Art REST API Fuzzing With LLMs in Cloud Service Computing RESTLess：利用云服务计算中的 LLM 增强最新 REST API 模糊测试

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-01 DOI: 10.1109/TSC.2024.3489441

Tao Zheng;Jiang Shao;Jinqiao Dai;Shuyu Jiang;Xingshu Chen;Changxiang Shen

{"title":"RESTLess: Enhancing State-of-the-Art REST API Fuzzing With LLMs in Cloud Service Computing","authors":"Tao Zheng;Jiang Shao;Jinqiao Dai;Shuyu Jiang;Xingshu Chen;Changxiang Shen","doi":"10.1109/TSC.2024.3489441","DOIUrl":"10.1109/TSC.2024.3489441","url":null,"abstract":"REST API Fuzzing is an emerging approach for automated vulnerability detection in cloud services. However, existing SOTA fuzzers face challenges in generating lengthy sequences comprising high-semantic requests, so that they may hardly trigger hard-to-reach states within a cloud service. To overcome this problem, we propose RESTLess, a flexible and efficient approach with hybrid optimization strategies for REST API fuzzing enhancement. Specifically, to pass the cloud gateway syntax semantic checking, we construct a dataset of valid parameters of REST API with Large Language Model named RTSet, then utilize it to develop an efficient REST API specification semantic enhancement approach. To detect vulnerability hidden under complex API operations, we design a flexible parameter rendering order optimization algorithm to increase the length and type of request sequences. Evaluation results highlight that RESTLess manifests noteworthy enhancements in the semantic quality of generated sequences in comparison to existing tools, thereby augmenting their capabilities in detecting vulnerabilities effectively. We also apply RESTLess to nine real-world cloud service such as Microsoft Azure, Amazon Web Services, Google Cloud, etc., and detecte 38 vulnerabilities, of which 16 have been confirmed and fixed by the relevant vendors.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4225-4238"},"PeriodicalIF":5.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563108","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}

引用次数: 0

Cost-Aware Dispersed Resource Probing and Offloading at the Edge: A User-Centric Online Layered Learning Approach 成本感知的边缘分散资源探测和卸载：以用户为中心的在线分层学习方法

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-01 DOI: 10.1109/TSC.2024.3489435

Tao Ouyang;Xu Chen;Liekang Zeng;Zhi Zhou

{"title":"Cost-Aware Dispersed Resource Probing and Offloading at the Edge: A User-Centric Online Layered Learning Approach","authors":"Tao Ouyang;Xu Chen;Liekang Zeng;Zhi Zhou","doi":"10.1109/TSC.2024.3489435","DOIUrl":"10.1109/TSC.2024.3489435","url":null,"abstract":"To meet the stringent requirement of edge intelligence applications, resource-constrained devices can offload their task to nearby resource-rich devices. Resource awareness, as a prime prerequisite for offloading decision-making, is critical for achieving efficient collaborative computation performance. Although major works have explored computation offloading in dynamic edge environments, the impact of fresh resource information perception has not been formally investigated. To bridge the gap, we design a cost-aware edge resource probing (CERP) framework for infrastructure-free edge computing, where a task device self-organizes its resource probing to enable informed computation offloading. We first formulate the joint optimization of device probing and offloading as a multi-stage optimal stopping problem and derive a multi-threshold-based optimal strategy with theoretical guarantees. Accordingly, we devise a data-driven layered learning mechanism to handle more complex real-world scenarios. The layered learning enables the task device to adaptively learn the optimal probing sequence and decision thresholds on the fly, aiming to strike a good balance between the gain of choosing the best edge device and the accumulated cost of deep resource probing. To further boost its learning efficiency, we replace the \u0000<inline-formula><tex-math>$epsilon$</tex-math></inline-formula>\u0000-greedy method with a tailored UCB-based adaptive exploration scheme in layered learning, thus better navigating the exploration and exploitation trade-off during probing processes. Finally, we conduct a thorough performance evaluation of the proposed CERP schemes using both extensive numerical simulations and realistic system prototype implementation, which demonstrate the superior performance of CERP in diverse application scenarios.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3270-3285"},"PeriodicalIF":5.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563110","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}

引用次数: 0

FedUP: Bridging Fairness and Efficiency in Cross-Silo Federated Learning FedUP：跨ilo 联合学习中的公平与效率之桥

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-01 DOI: 10.1109/TSC.2024.3489437

Haibo Liu;Jianfeng Lu;Xiong Wang;Chen Wang;Riheng Jia;Minglu Li

{"title":"FedUP: Bridging Fairness and Efficiency in Cross-Silo Federated Learning","authors":"Haibo Liu;Jianfeng Lu;Xiong Wang;Chen Wang;Riheng Jia;Minglu Li","doi":"10.1109/TSC.2024.3489437","DOIUrl":"10.1109/TSC.2024.3489437","url":null,"abstract":"Although federated learning (FL) enables collaborative training across multiple data silos in a privacy-protected manner, naively minimizing the aggregated loss to facilitate an efficient federation may compromise its fairness. Many efforts have been devoted to maintaining similar average accuracy across clients by reweighing the loss function while clients’ potential contributions are largely ignored. This, however, is often detrimental since treating all clients equally will harm the interests of those clients with more contribution. To tackle this issue, we introduce utopian fairness to expound the relationship between individual earning and collaborative productivity, and propose \u0000<underline>Fed\u0000erated-\u0000<underline>U\u0000to\u0000<underline>P\u0000ia (FedUP), a novel FL framework that balances both efficient collaboration and fair aggregation. For the distributed collaboration, we model the training process among strategic clients as a supermodular game, which facilitates a rational incentive design through the optimal reward. As for the model aggregation, we design a weight attention mechanism to compute the fair aggregation weights by minimizing the performance bias among heterogeneous clients. Particularly, we utilize the alternating optimization theory to bridge the gap between collaboration efficiency and utopian fairness, and theoretically prove that FedUP has fair model performance with fast-rate training convergence. Extensive experiments using both synthetic and real datasets demonstrate the superiority of FedUP.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3672-3684"},"PeriodicalIF":5.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563109","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}

引用次数: 0

Blockchain-Enabled HeartCare Framework for Cardiovascular Disease Diagnosis in Devices With Constrained Resources 利用区块链的心脏护理框架在资源有限的设备中进行心血管疾病诊断

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489442

Bidyut Bikash Borah;Khushboo Das;Geetartha Sarma;Soumik Roy;Dhruba Kumar Bhattacharyya

{"title":"Blockchain-Enabled HeartCare Framework for Cardiovascular Disease Diagnosis in Devices With Constrained Resources","authors":"Bidyut Bikash Borah;Khushboo Das;Geetartha Sarma;Soumik Roy;Dhruba Kumar Bhattacharyya","doi":"10.1109/TSC.2024.3489442","DOIUrl":"10.1109/TSC.2024.3489442","url":null,"abstract":"Cardiovascular diseases (CVDs) are the primary cause of mortality worldwide. The healthcare sector in India currently shows promise for substantial changes, specifically in the utilization and importance of the Internet of Medical Things (IoMT). Edge computing is necessary to make the IoMT more scalable, portable, reliable, and responsive. Security and privacy concerns impede the development and deployment of IoMT devices. The technology of blockchain can resolve security and privacy concerns. In this work, we implement a lightweight binary neural network (BNN) in a Cortex-M4 microcontroller (MCU) to enable the detection of four different types of heart illnesses present in a single-lead electrocardiogram (ECG) signal, in addition to proposing a blockchain-enabled HeartCare framework. The end-user can identify ailments and subsequently disseminate ECG results to medical professionals via a privacy-preserving blockchain-enabled framework. To acquire the ECG signal, a reusable fabric electrode was proposed and successfully fabricated. Finally, the BNN model is being trained utilising ECG databases of patients from the Indian continent, in addition to other state-of-the-art databases. The post-deployment validation of the proposed framework was conducted rigorously in alignment with the ACC/AHA Guidelines, resulting in an overall accuracy of 95.93% and a sensitivity of 95.90% for our BNN model.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3185-3198"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561888","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}

引用次数: 0

Flexible Computing: A New Framework for Improving Resource Allocation and Scheduling in Elastic Computing 灵活计算：改进弹性计算中资源分配和调度的新框架

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489433

Weipeng Cao;Jiongjiong Gu;Zhong Ming;Zhiyuan Cai;Yuzhao Wang;Changping Ji;Zhijiao Xiao;Yuhong Feng;Ye Liu;Liang-Jie Zhang

{"title":"Flexible Computing: A New Framework for Improving Resource Allocation and Scheduling in Elastic Computing","authors":"Weipeng Cao;Jiongjiong Gu;Zhong Ming;Zhiyuan Cai;Yuzhao Wang;Changping Ji;Zhijiao Xiao;Yuhong Feng;Ye Liu;Liang-Jie Zhang","doi":"10.1109/TSC.2024.3489433","DOIUrl":"10.1109/TSC.2024.3489433","url":null,"abstract":"Since the advent of cloud computing, Elastic Computing (EC) has become the standard architecture for resource allocation and scheduling. EC typically allocates computing resources based on predefined specifications, such as virtual machine or container flavors. However, these flavors are often constrained by fixed CPU-to-memory ratios, which frequently fail to match the actual resource needs of applications. As a result, cloud providers experience high resource allocation rates nearing saturation (<inline-formula><tex-math>$> $</tex-math></inline-formula>80%) but with low utilization (<inline-formula><tex-math>$< $</tex-math></inline-formula>25%). This study introduces Flexible Computing (FC), a novel approach to resource allocation and scheduling. Unlike EC, FC allocates resources based on an application resource usage profile, derived from the historical resource consumption of workloads, rather than relying on fixed specifications. Additionally, FC incorporates a real-time performance degradation detection mechanism to address performance issues caused by the noisy-neighbor effect when colocated workloads interfere with each other. FC dynamically adjusts resource allocation according to actual usage, ensuring that application performance meets Service Level Agreements (SLAs), while preventing resource waste and performance degradation from improper resource over-commitment. Large-scale experimental validations conducted on the FC architecture within Huawei Cloud data centers demonstrate that, compared to EC, FC can reduce computing resource consumption by over 33% while managing the same workloads. Furthermore, FC's real-time performance degradation detection model achieves a prediction error of less than 5% across various testing environments, highlighting its commercial viability.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"198-211"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561882","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}

引用次数: 0

No More Data Silos: Unified Microservice Failure Diagnosis With Temporal Knowledge Graph 不再有数据孤岛：利用时态知识图谱统一微服务故障诊断

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489444

Shenglin Zhang;Yongxin Zhao;Sibo Xia;Shirui Wei;Yongqian Sun;Chenyu Zhao;Shiyu Ma;Junhua Kuang;Bolin Zhu;Lemeng Pan;Yicheng Guo;Dan Pei

{"title":"No More Data Silos: Unified Microservice Failure Diagnosis With Temporal Knowledge Graph","authors":"Shenglin Zhang;Yongxin Zhao;Sibo Xia;Shirui Wei;Yongqian Sun;Chenyu Zhao;Shiyu Ma;Junhua Kuang;Bolin Zhu;Lemeng Pan;Yicheng Guo;Dan Pei","doi":"10.1109/TSC.2024.3489444","DOIUrl":"10.1109/TSC.2024.3489444","url":null,"abstract":"Microservices improve the scalability and flexibility of monolithic architectures to accommodate the evolution of software systems, but the complexity and dynamics of microservices challenge system reliability. Ensuring microservice quality requires efficient failure diagnosis, including detection and triage. Failure detection involves identifying anomalous behavior within the system, while triage entails classifying the failure type and directing it to the engineering team for resolution. Unfortunately, current approaches reliant on single-modal monitoring data, such as metrics, logs, or traces, cannot capture all failures and neglect interconnections among multimodal data, leading to erroneous diagnoses. Recent multimodal data fusion studies struggle to achieve deep integration, limiting diagnostic accuracy due to insufficiently captured interdependencies. Therefore, we propose \u0000<italic>UniDiag\u0000, which leverages temporal knowledge graphs to fuse multimodal data for effective failure diagnosis. \u0000<italic>UniDiag\u0000 applies a simple yet effective stream-based anomaly detection method to reduce computational cost and a novel microservice-oriented graph embedding method to represent the state of systems comprehensively. To assess the performance of \u0000<italic>UniDiag\u0000, we conduct extensive evaluation experiments using datasets from two benchmark microservice systems, demonstrating its superiority over existing methods and affirming the efficacy of multimodal data fusion. Additionally, we have publicly made the code and data available to facilitate further research.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4013-4026"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561889","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}

引用次数: 0

Efficient Public-Key Searchable Encryption Scheme From PSI With Scalable Proxy Servers 具有可扩展代理服务器的 PSI 高效公钥可搜索加密方案

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489432

Xiangqian Kong;Lanxiang Chen;Yizhao Zhu;Yi Mu

{"title":"Efficient Public-Key Searchable Encryption Scheme From PSI With Scalable Proxy Servers","authors":"Xiangqian Kong;Lanxiang Chen;Yizhao Zhu;Yi Mu","doi":"10.1109/TSC.2024.3489432","DOIUrl":"10.1109/TSC.2024.3489432","url":null,"abstract":"Public-key Encryption with Keyword Search (PEKS) enables secure keyword searches within encrypted data. At the same time, Public-key Authenticated Encryption with Keyword Search (PAEKS) enhances security by permitting authorized users to search specific keyword sets, protecting against Internal Keyword Guessing Attacks (IKGA). However, to the best of our knowledge, existing PEKS and PAEKS schemes typically require to generate a distinct set of keyword ciphertext for each data user, leading to storage, computation, and communication costs and the lack of support for multiple-keyword search. In this article, we introduce a novel, efficient public-key searchable encryption scheme from the private set intersection (PSI) with scalable proxy servers, using a PSI protocol with multiple proxy server settings, which achieves sub-linear complexity. Our scheme is secure against IKGA and supports multiple keyword searches and sharing one encrypted keyword set by multiple users. We introduce an efficient system model with scalable proxy servers, significantly reducing computational overhead through a divide-and-conquer approach. Our proposed scheme supports multiple data users, and multiple keyword searches, utilizing a single set of keyword ciphertext for multiple data users. We formally define a security model and present a comprehensive security proof to demonstrate that our scheme maintains ciphertext-indistinguishability and trapdoor-indistinguishability.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3527-3540"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561887","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}

引用次数: 0

Multi-Objective Deep Reinforcement Learning for Function Offloading in Serverless Edge Computing 用于无服务器边缘计算功能卸载的多目标深度强化学习

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489443

Yaning Yang;Xiao Du;Yutong Ye;Jiepin Ding;Ting Wang;Mingsong Chen;Keqin Li

{"title":"Multi-Objective Deep Reinforcement Learning for Function Offloading in Serverless Edge Computing","authors":"Yaning Yang;Xiao Du;Yutong Ye;Jiepin Ding;Ting Wang;Mingsong Chen;Keqin Li","doi":"10.1109/TSC.2024.3489443","DOIUrl":"10.1109/TSC.2024.3489443","url":null,"abstract":"Function offloading problems play a crucial role in optimizing the performance of applications in serverless edge computing (SEC). Existing research has extensively explored function offloading strategies based on optimizing a single objective. However, a significant challenge arises when users expect to optimize multiple objectives according to the relative importance of these objectives. This challenge becomes particularly pronounced when the relative importance of the objectives dynamically shifts. Consequently, there is an urgent need for research into multi-objective function offloading methods. In this paper, we redefine the SEC function offloading problem as a dynamic multi-objective optimization issue and propose a novel approach based on Multi-objective Reinforcement Learning (MORL) called MOSEC. MOSEC can coordinately optimize three objectives, i.e., application completion time, User Device (UD) energy consumption, and user cost. To reduce the impact of extrapolation errors, MOSEC integrates a Near-on Experience Replay (NER) strategy during the model training. Furthermore, MOSEC adopts our proposed Earliest First (EF) scheme to maintain the policies learned previously, which can efficiently mitigate the catastrophic policy forgetting problem. Extensive experiments conducted on various generated applications demonstrate the superiority of MOSEC over state-of-the-art multi-objective optimization algorithms.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"288-301"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561883","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}

引用次数: 0

DeFiGuard: A Price Manipulation Detection Service in DeFi Using Graph Neural Networks DeFiGuard：使用图神经网络的 DeFi 价格操纵检测服务

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-10-31 DOI: 10.1109/TSC.2024.3489439

Dabao Wang;Bang Wu;Xingliang Yuan;Lei Wu;Yajin Zhou;Helei Cui

{"title":"DeFiGuard: A Price Manipulation Detection Service in DeFi Using Graph Neural Networks","authors":"Dabao Wang;Bang Wu;Xingliang Yuan;Lei Wu;Yajin Zhou;Helei Cui","doi":"10.1109/TSC.2024.3489439","DOIUrl":"10.1109/TSC.2024.3489439","url":null,"abstract":"The prosperity of Decentralized Finance (DeFi) unveils underlying risks, with reported losses surpassing 3.2 billion USD between 2018 and 2022 due to vulnerabilities in Decentralized Applications (DApps). One significant threat is the Price Manipulation Attack (PMA) that alters asset prices during transaction execution. As a result, PMA accounts for over 50 million USD in losses. To address the urgent need for efficient PMA detection, this article introduces a novel detection service, \u0000<italic>DeFiGuard\u0000, using Graph Neural Networks (GNNs). In this article, we propose cash flow graphs with four distinct features, which capture the trading behaviors from transactions. Moreover, \u0000<italic>DeFiGuard\u0000 integrates transaction parsing, graph construction, model training, and PMA detection. Evaluations on the collected transactions demonstrate that \u0000<italic>DeFiGuard\u0000 with GNN models outperforms the baseline MLP model and classical classification models in Accuracy, TPR, FPR, and AUC-ROC. The results of ablation studies suggest that the combination of the four proposed node features enhances \u0000<italic>DeFiGuard\u0000 ’s efficacy. Moreover, \u0000<italic>DeFiGuard\u0000 classifies transactions within 0.892 to 5.317 seconds, which provides sufficient time for the victims (DApps and users) to take action to rescue their vulnerable funds. In conclusion, this research offers a significant step towards safeguarding the DeFi landscape from PMAs using GNNs.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3345-3358"},"PeriodicalIF":5.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561885","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}

引用次数: 0