Pub Date : 2024-10-22DOI: 10.1016/j.future.2024.107567
Weina Niu , Kexuan Zhang , Ran Yan , Jie Li , Yan Zhang , Xiaosong Zhang
Return Oriented Programming (ROP) is one of the most challenging threats to operating systems. Traditional detection and defense techniques for ROP such as stack protection, address randomization, compiler optimization, control flow integrity, and basic block thresholds have certain limitations in accuracy or efficiency. At the same time, they cannot effectively detect ROP variant attacks, such as COP, COOP, JOP. In this paper, we propose a novel ROP and its variants detection approach that first filters the normal execution flow according to four strategies provided and then adopts Graph Matching Network (GMN) to determine whether there is ROP or its variant attack. Moreover, we developed a prototype named ROPGMN with shared memory to solve cross-language and cross-process problems. Using real-world vulnerable programs and constructed programs with dangerous function calls, we conduct extensive experiments with 6 ROP detectors to evaluate ROPGMN. The experimental results demonstrate the effectiveness of ROPGMN in discovering ROPs and their variant attacks with low performance overhead.
{"title":"ROPGMN: Effective ROP and variants discovery using dynamic feature and graph matching network","authors":"Weina Niu , Kexuan Zhang , Ran Yan , Jie Li , Yan Zhang , Xiaosong Zhang","doi":"10.1016/j.future.2024.107567","DOIUrl":"10.1016/j.future.2024.107567","url":null,"abstract":"<div><div>Return Oriented Programming (ROP) is one of the most challenging threats to operating systems. Traditional detection and defense techniques for ROP such as stack protection, address randomization, compiler optimization, control flow integrity, and basic block thresholds have certain limitations in accuracy or efficiency. At the same time, they cannot effectively detect ROP variant attacks, such as COP, COOP, JOP. In this paper, we propose a novel ROP and its variants detection approach that first filters the normal execution flow according to four strategies provided and then adopts Graph Matching Network (GMN) to determine whether there is ROP or its variant attack. Moreover, we developed a prototype named ROPGMN with shared memory to solve cross-language and cross-process problems. Using real-world vulnerable programs and constructed programs with dangerous function calls, we conduct extensive experiments with 6 ROP detectors to evaluate ROPGMN. The experimental results demonstrate the effectiveness of ROPGMN in discovering ROPs and their variant attacks with low performance overhead.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107567"},"PeriodicalIF":6.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560681","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}
Smart contracts have played a pivotal role in the evolution of blockchains and Decentralized Applications (DApps). As DApps continue to gain widespread adoption, multiple smart contract languages have been and are being made available to developers, each with its distinctive features, strengths, and weaknesses. In this paper, we examine the smart contract languages used in major blockchain platforms, with the goal of providing a comprehensive assessment of their main properties. Our analysis targets the programming languages rather than the underlying architecture: as a result, while we do consider the interplay between language design and blockchain model, our main focus remains on language-specific features such as usability, programming style, safety and security. To conduct our assessment, we propose an original benchmark which encompasses a wide, yet manageable, spectrum of key use cases that cut across all the smart contract languages under examination.
{"title":"Smart contract languages: A comparative analysis","authors":"Massimo Bartoletti , Lorenzo Benetollo , Michele Bugliesi , Silvia Crafa , Giacomo Dal Sasso , Roberto Pettinau , Andrea Pinna , Mattia Piras , Sabina Rossi , Stefano Salis , Alvise Spanò , Viacheslav Tkachenko , Roberto Tonelli , Roberto Zunino","doi":"10.1016/j.future.2024.107563","DOIUrl":"10.1016/j.future.2024.107563","url":null,"abstract":"<div><div>Smart contracts have played a pivotal role in the evolution of blockchains and Decentralized Applications (DApps). As DApps continue to gain widespread adoption, multiple smart contract languages have been and are being made available to developers, each with its distinctive features, strengths, and weaknesses. In this paper, we examine the smart contract languages used in major blockchain platforms, with the goal of providing a comprehensive assessment of their main properties. Our analysis targets the programming languages rather than the underlying architecture: as a result, while we do consider the interplay between language design and blockchain model, our main focus remains on language-specific features such as usability, programming style, safety and security. To conduct our assessment, we propose an original benchmark which encompasses a wide, yet manageable, spectrum of key use cases that cut across all the smart contract languages under examination.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107563"},"PeriodicalIF":6.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554218","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}
Pub Date : 2024-10-21DOI: 10.1016/j.future.2024.107565
Jiyang Wang, Jifeng Che, Zhiwu Li, Jialu Gao, Linyue Zhang
Wind speed time series forecasting has been widely used in wind power generation. However, the nonlinear and non-stationary characteristics of wind speed make accurate wind speed forecasting a difficult task. In recent years, the rapid development of artificial intelligence and machine learning technology provides a new solution to the problem of wind speed forecasting. Combining the advance of artificial intelligence and data analysis strategy, this paper proposes a wind speed forecasting system based on multi-model fusion and integrated learning. Considering the differences in data observation and training principles of various algorithms and exploiting the advantages of each model, a wind speed forecasting system with multiple machine learning algorithms embedded in integrated learning is constructed, which includes three modules: data preprocessing, optimization and forecasting. The data preprocessing module can conduct quantitative analysis through input data decomposition and feature extraction, and the combination of multi-objective intelligent optimization algorithm and combined forecasting method can effectively forecast the wind speed time series. The validity of the algorithm is verified using the data of Shandong wind farm in China. The forecasting results show that compared with the traditional single model forecasting, the proposed integrated wind speed forecasting system based on the fusion of multiple models has higher forecasting accuracy.
{"title":"Hybrid wind speed optimization forecasting system based on linear and nonlinear deep neural network structure and data preprocessing fusion","authors":"Jiyang Wang, Jifeng Che, Zhiwu Li, Jialu Gao, Linyue Zhang","doi":"10.1016/j.future.2024.107565","DOIUrl":"10.1016/j.future.2024.107565","url":null,"abstract":"<div><div>Wind speed time series forecasting has been widely used in wind power generation. However, the nonlinear and non-stationary characteristics of wind speed make accurate wind speed forecasting a difficult task. In recent years, the rapid development of artificial intelligence and machine learning technology provides a new solution to the problem of wind speed forecasting. Combining the advance of artificial intelligence and data analysis strategy, this paper proposes a wind speed forecasting system based on multi-model fusion and integrated learning. Considering the differences in data observation and training principles of various algorithms and exploiting the advantages of each model, a wind speed forecasting system with multiple machine learning algorithms embedded in integrated learning is constructed, which includes three modules: data preprocessing, optimization and forecasting. The data preprocessing module can conduct quantitative analysis through input data decomposition and feature extraction, and the combination of multi-objective intelligent optimization algorithm and combined forecasting method can effectively forecast the wind speed time series. The validity of the algorithm is verified using the data of Shandong wind farm in China. The forecasting results show that compared with the traditional single model forecasting, the proposed integrated wind speed forecasting system based on the fusion of multiple models has higher forecasting accuracy.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107565"},"PeriodicalIF":6.2,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560682","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}
Pub Date : 2024-10-21DOI: 10.1016/j.future.2024.107573
Adel N. Toosi , Bahman Javadi , Alexandru Iosup , Evgenia Smirni , Schahram Dustdar
Serverless computing is a cloud computing model that abstracts server management, allowing developers to focus solely on writing code without concerns about the underlying infrastructure. This paradigm shift is transforming application development by reducing time to market, lowering costs, and enhancing scalability. In serverless computing, functions are event-driven and automatically scale in response to events such as data changes or user requests. Despite its advantages, serverless computing presents several research challenges, including managing state for ephemeral functions, mitigating cold start delays, optimizing function composition, debugging, efficient auto-scaling, resource management, and ensuring security and compliance. This special issue focused on addressing these challenges by promoting research on innovative solutions and exploring the potential of serverless computing in new application domains.
{"title":"Serverless Computing for Next-generation Application Development","authors":"Adel N. Toosi , Bahman Javadi , Alexandru Iosup , Evgenia Smirni , Schahram Dustdar","doi":"10.1016/j.future.2024.107573","DOIUrl":"10.1016/j.future.2024.107573","url":null,"abstract":"<div><div>Serverless computing is a cloud computing model that abstracts server management, allowing developers to focus solely on writing code without concerns about the underlying infrastructure. This paradigm shift is transforming application development by reducing time to market, lowering costs, and enhancing scalability. In serverless computing, functions are event-driven and automatically scale in response to events such as data changes or user requests. Despite its advantages, serverless computing presents several research challenges, including managing state for ephemeral functions, mitigating cold start delays, optimizing function composition, debugging, efficient auto-scaling, resource management, and ensuring security and compliance. This special issue focused on addressing these challenges by promoting research on innovative solutions and exploring the potential of serverless computing in new application domains.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107573"},"PeriodicalIF":6.2,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651754","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}
Pub Date : 2024-10-21DOI: 10.1016/j.future.2024.107555
Sadia Jabeen Siddiqi , Sana Saleh , Mian Ahmad Jan , Muhammad Tariq
The rapid evolution of vehicular communication technologies in recent times necessitates robust security measures and enhanced road safety protocols. Integrity of data shared between vehicles, their Digital Twins (DT) and road side units is at stake. Intrusion in these data can potentially lead to misinformation in Advanced Driving Assistance Systems (ADAS) causing serious consequences upon road safety. These include improper detection of drunk driving behaviors. In this domain, Web 3.0 emerges as the overarching approach that can transform security of vehicles and ensure road safety. This paper explores the potential of Web 3.0 and its key enabling technologies to establish a VEhicular meTAVERSE (Vetaverse) utilizing edge-based DTs of the vehicles to process their dynamics shared in real-time, and based on its deep learning models, predict whether the driving behavior is drunk or sober. This Deep Neural Network (DNN) performs these predictions with 96% accuracy. It secures all Vehicle-to-Digital Twin (V2DT) communications via Multichain - a horizontally scaled parallel blockchains platform that tamper proofs each bit of sensor data, and optimizes transaction validation time to leverage vetaverse security. Results reveal that this framework is accurate and computationally lightweight in comparison to existing state-of-the-art, and brings Web 3.0 to the crucial road safety use-case.
近来，车辆通信技术发展迅速，需要采取强有力的安全措施，并加强道路安全协议。车辆、其数字孪生系统 (DT) 和路侧装置之间共享数据的完整性岌岌可危。对这些数据的入侵有可能导致高级驾驶辅助系统（ADAS）中的错误信息，给道路安全带来严重后果。其中包括对酒后驾驶行为的不当检测。在这一领域，Web 3.0 成为改变车辆安全和确保道路安全的首要方法。本文探讨了 Web 3.0 及其关键使能技术的潜力，以利用基于边缘的车辆 DT 来建立一个 VEhicular meTAVERSE（Vetaverse），实时处理共享的车辆动态，并根据其深度学习模型来预测驾驶行为是醉酒还是清醒。该深度神经网络（DNN）的预测准确率高达 96%。它通过 Multichain（一个横向扩展的并行区块链平台）确保所有车辆到数字双胞胎（V2DT）通信的安全，Multichain 可对每个比特的传感器数据进行防篡改验证，并优化交易验证时间，以充分利用vetaverse安全性。研究结果表明，与现有的最先进技术相比，该框架准确度高、计算量小，并将 Web 3.0 带到了关键的道路安全应用案例中。
{"title":"Securing the vetaverse: Web 3.0 for decentralized Digital Twin-enhanced vehicle–road safety","authors":"Sadia Jabeen Siddiqi , Sana Saleh , Mian Ahmad Jan , Muhammad Tariq","doi":"10.1016/j.future.2024.107555","DOIUrl":"10.1016/j.future.2024.107555","url":null,"abstract":"<div><div>The rapid evolution of vehicular communication technologies in recent times necessitates robust security measures and enhanced road safety protocols. Integrity of data shared between vehicles, their Digital Twins (DT) and road side units is at stake. Intrusion in these data can potentially lead to misinformation in Advanced Driving Assistance Systems (ADAS) causing serious consequences upon road safety. These include improper detection of drunk driving behaviors. In this domain, Web 3.0 emerges as the overarching approach that can transform security of vehicles and ensure road safety. This paper explores the potential of Web 3.0 and its key enabling technologies to establish a VEhicular meTAVERSE (Vetaverse) utilizing edge-based DTs of the vehicles to process their dynamics shared in real-time, and based on its deep learning models, predict whether the driving behavior is drunk or sober. This Deep Neural Network (DNN) performs these predictions with 96% accuracy. It secures all Vehicle-to-Digital Twin (V2DT) communications via Multichain - a horizontally scaled parallel blockchains platform that tamper proofs each bit of sensor data, and optimizes transaction validation time to leverage vetaverse security. Results reveal that this framework is accurate and computationally lightweight in comparison to existing state-of-the-art, and brings Web 3.0 to the crucial road safety use-case.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107555"},"PeriodicalIF":6.2,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526099","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}
Pub Date : 2024-10-20DOI: 10.1016/j.future.2024.107568
YueTong Wu , Hu Xiong , Fazlullah Khan , Salman Ijaz , Ryan Alturki , Abeer Aljohani
Web 3.0 is a grand design with intricate data interchange, implying the requirement of versatile network protocol to ensure its security. Attribute-based signature (ABS) allows a user, who is featured with a set of attributes, to sign messages under a predicate. The validity of the ABS signature demonstrates that this signature is generated by the user whose attributes satisfy the corresponding predicate, and thus flexibly achieves anonymous authentication. Similar to other digital signatures, the security of ABS is broken in case the private key of the user is leaked out. To address the threat brought by the key leakage, this paper proposes a puncturable attribute-based signature scheme that allows the private key generator to revoke the signing right associated with specific tags. This paper firstly elaborates the construction of the proposed ABS scheme with puncturable property, and then proves its security theoretically by reducing the involved security to the computational Diffie–Hellman assumption. This paper then experimentally shows that the suggested puncturable ABS scheme owns a more efficient storage cost and superior performance.
{"title":"Efficient and provably secured puncturable attribute-based signature for Web 3.0","authors":"YueTong Wu , Hu Xiong , Fazlullah Khan , Salman Ijaz , Ryan Alturki , Abeer Aljohani","doi":"10.1016/j.future.2024.107568","DOIUrl":"10.1016/j.future.2024.107568","url":null,"abstract":"<div><div>Web 3.0 is a grand design with intricate data interchange, implying the requirement of versatile network protocol to ensure its security. Attribute-based signature (ABS) allows a user, who is featured with a set of attributes, to sign messages under a predicate. The validity of the ABS signature demonstrates that this signature is generated by the user whose attributes satisfy the corresponding predicate, and thus flexibly achieves anonymous authentication. Similar to other digital signatures, the security of ABS is broken in case the private key of the user is leaked out. To address the threat brought by the key leakage, this paper proposes a puncturable attribute-based signature scheme that allows the private key generator to revoke the signing right associated with specific tags. This paper firstly elaborates the construction of the proposed ABS scheme with puncturable property, and then proves its security theoretically by reducing the involved security to the computational Diffie–Hellman assumption. This paper then experimentally shows that the suggested puncturable ABS scheme owns a more efficient storage cost and superior performance.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107568"},"PeriodicalIF":6.2,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526100","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}
Pub Date : 2024-10-19DOI: 10.1016/j.future.2024.107560
Wei Cao , Wenting Shen , Jing Qin , Hao Lin
With the rapid development of cloud computing, outsourcing massive data and complex deep learning model to cloud servers (CSs) has become a popular trend, which also brings some security problems. One is that the model stored in the CSs may be corrupted, leading to incorrect inference and training results. The other is that the privacy of outsourced data and model may be compromised. However, existing privacy-preserving and verifiable inference schemes suffer from low detection probability, high communication overhead and substantial computational time. To solve the above problems, we propose a privacy-preserving and verifiable scheme for convolutional neural network inference and training in cloud computing. In our scheme, the model owner generates the authenticators for model parameters before uploading the model to CSs. In the phase of model integrity verification, model owner and user can utilize these authenticators to check model integrity with high detection probability. Furthermore, we design a set of privacy-preserving protocols based on replicated secret sharing for both the inference and training phases, significantly reducing communication overhead and computational time. Through security analysis, we demonstrate that our scheme is secure. Experimental evaluations show that the proposed scheme outperforms existing schemes in privacy-preserving inference and model integrity verification.
{"title":"Privacy-preserving and verifiable convolution neural network inference and training in cloud computing","authors":"Wei Cao , Wenting Shen , Jing Qin , Hao Lin","doi":"10.1016/j.future.2024.107560","DOIUrl":"10.1016/j.future.2024.107560","url":null,"abstract":"<div><div>With the rapid development of cloud computing, outsourcing massive data and complex deep learning model to cloud servers (CSs) has become a popular trend, which also brings some security problems. One is that the model stored in the CSs may be corrupted, leading to incorrect inference and training results. The other is that the privacy of outsourced data and model may be compromised. However, existing privacy-preserving and verifiable inference schemes suffer from low detection probability, high communication overhead and substantial computational time. To solve the above problems, we propose a privacy-preserving and verifiable scheme for convolutional neural network inference and training in cloud computing. In our scheme, the model owner generates the authenticators for model parameters before uploading the model to CSs. In the phase of model integrity verification, model owner and user can utilize these authenticators to check model integrity with high detection probability. Furthermore, we design a set of privacy-preserving protocols based on replicated secret sharing for both the inference and training phases, significantly reducing communication overhead and computational time. Through security analysis, we demonstrate that our scheme is secure. Experimental evaluations show that the proposed scheme outperforms existing schemes in privacy-preserving inference and model integrity verification.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107560"},"PeriodicalIF":6.2,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554220","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}
Pub Date : 2024-10-19DOI: 10.1016/j.future.2024.107562
Dincy R. Arikkat , Mert Cihangiroglu , Mauro Conti , Rafidha Rehiman K.A. , Serena Nicolazzo , Antonino Nocera , Vinod P.
The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. Organizations are inadvertently enlarging their vulnerability to cyber threats by integrating more interconnected devices into their operations, which makes these threats both more sophisticated and more common. Consequently, organizations have been compelled to seek innovative approaches to mitigate the menaces inherent in their infrastructure. In response, considerable research efforts have been directed towards creating effective solutions for sharing Cyber Threat Intelligence (CTI). Current information-sharing methods lack privacy safeguards, leaving organizations vulnerable to proprietary and confidential data leaks. To tackle this problem, we designed a novel framework called SeCTIS (Secure Cyber Threat Intelligence Sharing), integrating Swarm Learning and Blockchain technologies to enable businesses to collaborate, preserving the privacy of their CTI data. Moreover, our approach provides a way to assess the data and model quality and the trustworthiness of all the participants leveraging some validators through Zero Knowledge Proofs. Extensive experimentation has confirmed the accuracy and performance of our framework. Furthermore, our detailed attack model analyzes its resistance to attacks that could impact data and model quality.
{"title":"SeCTIS: A framework to Secure CTI Sharing","authors":"Dincy R. Arikkat , Mert Cihangiroglu , Mauro Conti , Rafidha Rehiman K.A. , Serena Nicolazzo , Antonino Nocera , Vinod P.","doi":"10.1016/j.future.2024.107562","DOIUrl":"10.1016/j.future.2024.107562","url":null,"abstract":"<div><div>The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. Organizations are inadvertently enlarging their vulnerability to cyber threats by integrating more interconnected devices into their operations, which makes these threats both more sophisticated and more common. Consequently, organizations have been compelled to seek innovative approaches to mitigate the menaces inherent in their infrastructure. In response, considerable research efforts have been directed towards creating effective solutions for sharing Cyber Threat Intelligence (CTI). Current information-sharing methods lack privacy safeguards, leaving organizations vulnerable to proprietary and confidential data leaks. To tackle this problem, we designed a novel framework called SeCTIS (Secure Cyber Threat Intelligence Sharing), integrating Swarm Learning and Blockchain technologies to enable businesses to collaborate, preserving the privacy of their CTI data. Moreover, our approach provides a way to assess the data and model quality and the trustworthiness of all the participants leveraging some <em>validators</em> through Zero Knowledge Proofs. Extensive experimentation has confirmed the accuracy and performance of our framework. Furthermore, our detailed attack model analyzes its resistance to attacks that could impact data and model quality.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107562"},"PeriodicalIF":6.2,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573266","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}
Pub Date : 2024-10-19DOI: 10.1016/j.future.2024.107558
Han Cao , Jinquan Zhang , Long Chen , Siyuan Li , Guang Shi
Serverless computing represents an emerging paradigm within cloud computing, characterized by the fundamental concept of enabling developers to run applications without the need for concerns related to the management of underlying servers. Although there are several mature serverless computing platforms currently exist, there is limited availability of open-source simulation platforms that can accurately simulate the characteristics of serverless environments and provide a free and convenient tool for researchers to conduct investigations. Furthermore, existing simulation platforms do not provide comprehensive interfaces for scheduling strategies and do not provide the diverse monitoring metrics required by researchers. In response to this gap, we have developed the ServlessSimPro simulation platform. This platform offers the most comprehensive set of scheduling algorithms, diverse evaluation metrics, and extensive interfaces and parameters among all existing serverless simulators. The experimental results demonstrate that the scheduling algorithm of the simulator can effectively reduce latency, enhance resource utilization, and decrease energy consumption.
{"title":"ServlessSimPro: A comprehensive serverless simulation platform","authors":"Han Cao , Jinquan Zhang , Long Chen , Siyuan Li , Guang Shi","doi":"10.1016/j.future.2024.107558","DOIUrl":"10.1016/j.future.2024.107558","url":null,"abstract":"<div><div>Serverless computing represents an emerging paradigm within cloud computing, characterized by the fundamental concept of enabling developers to run applications without the need for concerns related to the management of underlying servers. Although there are several mature serverless computing platforms currently exist, there is limited availability of open-source simulation platforms that can accurately simulate the characteristics of serverless environments and provide a free and convenient tool for researchers to conduct investigations. Furthermore, existing simulation platforms do not provide comprehensive interfaces for scheduling strategies and do not provide the diverse monitoring metrics required by researchers. In response to this gap, we have developed the ServlessSimPro simulation platform. This platform offers the most comprehensive set of scheduling algorithms, diverse evaluation metrics, and extensive interfaces and parameters among all existing serverless simulators. The experimental results demonstrate that the scheduling algorithm of the simulator can effectively reduce latency, enhance resource utilization, and decrease energy consumption.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"163 ","pages":"Article 107558"},"PeriodicalIF":6.2,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533496","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}
Pub Date : 2024-10-19DOI: 10.1016/j.future.2024.107559
Zongpu Wei, Jinsong Wang, Zening Zhao, Kai Shi
Anomaly detection in edge–cloud scenarios stands as a critical means to ensure the security of network environment. Federated learning (FL)-based anomaly detection combines multiple data sources and ensures data privacy, making it a promising distributed detection method. However, FL-based anomaly detection system is usually affected by data heterogeneity and data bias, resulting in the inefficiency of data used for FL and the decline of detection performance. We propose an iterative federated clustering ensemble algorithm named IFCEA, in which we (1) establish a committee on the devices, and select the optimal participation for each device based on the evaluations of committee; (2) filter the clusters based on committee results, and exclude the biased clusters; (3) design an aggregation weight that reflects the degree of local distribution balance; (4) present a novel cluster initialization method, OneBiPartition, which adapts to the number of clusters and commences clustering federated task efficiently. IFCEA enhances the data quality used in FL-based anomaly detection system from two perspectives: device selection and participation weights, effectively addressing the issues of data heterogeneity and data bias faced during the FL training phase. Extensive experimental results on five network traffic datasets (the UNSW-NB15, CIC-IDS2017, CIC-IDS2018, CIC-DDoS2019 and BCCC-DDoS2024 datasets) demonstrate that our proposed framework outperforms in terms of detection metrics and convergence performance.
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