High-Confidence Computing最新文献_第7页

Spider monkey optimization based resource allocation and scheduling in fog computing environment 雾计算环境下基于蜘蛛猴优化的资源分配与调度

High-Confidence Computing Pub Date : 2023-09-01 DOI: 10.1016/j.hcc.2023.100149

Shahid Sultan Hajam, Shabir Ahmad Sofi

{"title":"Spider monkey optimization based resource allocation and scheduling in fog computing environment","authors":"Shahid Sultan Hajam, Shabir Ahmad Sofi","doi":"10.1016/j.hcc.2023.100149","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100149","url":null,"abstract":"<div><p>Spider monkey optimization (SMO) is a quite popular and recent swarm intelligence algorithm for numerical optimization. SMO is Fission-Fusion social structure based algorithm inspired by spider monkey’s behavior. The algorithm proves to be very efficient in solving various constrained and unconstrained optimization problems. This paper presents the application of SMO in fog computing. We propose a heuristic initialization based spider monkey optimization algorithm for resource allocation and scheduling in a fog computing network. The algorithm minimizes the total cost (service time and monetary cost) of tasks by choosing the optimal fog nodes. Longest job fastest processor (LJFP), shortest job fastest processor (SJFP), and minimum completion time (MCT) based initialization of SMO are proposed and compared with each other. The performance is compared based on the parameters of average cost, average service time, average monetary cost, and the average cost per schedule. The results demonstrate the efficacy of MCT-SMO as compared to other heuristic initialization based SMO algorithms and Particle Swarm Optimization (PSO).</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 3","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50191223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Trustworthy decentralized collaborative learning for edge intelligence: A survey 值得信赖的边缘智能去中心化协作学习：一项调查

High-Confidence Computing Pub Date : 2023-09-01 DOI: 10.1016/j.hcc.2023.100150

Dongxiao Yu , Zhenzhen Xie , Yuan Yuan , Shuzhen Chen , Jing Qiao , Yangyang Wang , Yong Yu , Yifei Zou , Xiao Zhang

{"title":"Trustworthy decentralized collaborative learning for edge intelligence: A survey","authors":"Dongxiao Yu , Zhenzhen Xie , Yuan Yuan , Shuzhen Chen , Jing Qiao , Yangyang Wang , Yong Yu , Yifei Zou , Xiao Zhang","doi":"10.1016/j.hcc.2023.100150","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100150","url":null,"abstract":"<div><p>Edge intelligence is an emerging technology that enables artificial intelligence on connected systems and devices in close proximity to the data sources. decentralized collaborative learning (DCL) is a novel edge intelligence technique that allows distributed clients to cooperatively train a global learning model without revealing their data. DCL has a wide range of applications in various domains, such as smart city and autonomous driving. However, DCL faces significant challenges in ensuring its trustworthiness, as data isolation and privacy issues make DCL systems vulnerable to adversarial attacks that aim to breach system confidentiality, undermine learning reliability or violate data privacy. Therefore, it is crucial to design DCL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we present a comprehensive review of existing efforts for designing trustworthy DCL systems from the three key aformentioned aspects: security, robustness, and privacy. We analyze the threats that affect the trustworthiness of DCL across different scenarios and assess specific technical solutions for achieving each aspect of trustworthy DCL (TDCL). Finally, we highlight open challenges and future directions for advancing TDCL research and practice.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 3","pages":"Article 100150"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50191158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An energy-efficient resource allocation strategy in massive MIMO-enabled vehicular edge computing networks 大规模MIMO车载边缘计算网络中的节能资源分配策略

High-Confidence Computing Pub Date : 2023-09-01 DOI: 10.1016/j.hcc.2023.100130

Yibin Xie , Lei Shi , Zhenchun Wei , Juan Xu , Yang Zhang

引用次数: 0

Reaching consensus for membership dynamic in secret sharing and its application to cross-chain 秘密共享中成员动态达成共识及其在跨链中的应用

High-Confidence Computing Pub Date : 2023-09-01 DOI: 10.1016/j.hcc.2023.100131

Yan Zhu , Bingyu Li , Zhenyang Ding , Yang Yang , Qianhong Wu , Haibin Zheng

{"title":"Reaching consensus for membership dynamic in secret sharing and its application to cross-chain","authors":"Yan Zhu , Bingyu Li , Zhenyang Ding , Yang Yang , Qianhong Wu , Haibin Zheng","doi":"10.1016/j.hcc.2023.100131","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100131","url":null,"abstract":"<div><p>The communication efficiency optimization, censorship resilience, and generation of shared randomness are inseparable from the threshold cryptography in the existing Byzantine Fault Tolerant (BFT) consensus. The membership in consensus in a blockchain scenario supports dynamic changes, which effectively prevents the corruption of consensus participants. Especially in cross-chain protocols, the dynamic access to different blockchains will inevitably bring about the demand for member dynamic. Most existing threshold cryptography schemes rely on redefined key shares, leading to a static set of secret sharing participants. In this paper, we propose a general approach to coupling blockchain consensus and dynamic secret sharing. The committee performs consensus confirmation of both dynamic secret sharing and transaction proposals. Our scheme facilitates threshold cryptography membership dynamic, thus underlying support for membership dynamic of threshold cryptography-based BFT consensus schemes. We instantiate a dynamic HotStuff consensus to demonstrate the effectiveness of the scheme. After the correctness and security proof, our scheme achieves the secrecy and integrity of the threshold key shares while ensuring consensus liveness and safety. Experimental results prove that our scheme obtains dynamic membership with negligible overhead.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 3","pages":"Article 100131"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50191219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UltraJam: Ultrasonic adaptive jammer based on nonlinearity effect of microphone circuits UltraJam：基于麦克风电路非线性效应的超声自适应干扰器

High-Confidence Computing Pub Date : 2023-09-01 DOI: 10.1016/j.hcc.2023.100129

Zhicheng Han , Jun Ma , Chao Xu , Guoming Zhang

{"title":"UltraJam: Ultrasonic adaptive jammer based on nonlinearity effect of microphone circuits","authors":"Zhicheng Han , Jun Ma , Chao Xu , Guoming Zhang","doi":"10.1016/j.hcc.2023.100129","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100129","url":null,"abstract":"<div><p>The widely used devices (e.g. smartphones, recorders) equipped with microphones have posed a severe threat to confidential conversations. In this paper, we design an inaudible anti-eavesdropping method: UltraJam, to reduce the risk of unwanted and secret recordings. UltraJam uses the ultrasonic signal to mask conversation. By leveraging the nonlinear effect of microphone circuits, the adaptive ultrasonic signal can be recorded and demodulated into low-frequency which can effectively squash the sound. Based on the characteristics of the attenuation coefficient and frequency response, we construct a number of jamming signals with different bandwidths and designed a wideband signal injection array, meanwhile adaptively adjust the power at each bandwidth signal to cover more frequency bands and increase usage scenarios. To verify the security of the microphone jamming system, we also utilize several audio recovery methods to recover the raw signal from jamming noise. The experimental results show that less than 1% of the words are recognized in the jamming recording, and even with the audio recovery method, 99% of the words still cannot be recognized in the recovered jamming recording.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 3","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50191220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BC driven IoT-based food quality traceability system for dairy product using deep learning model 基于深度学习模型的BC驱动的基于物联网的乳制品食品质量追溯系统

High-Confidence Computing Pub Date : 2023-09-01 DOI: 10.1016/j.hcc.2023.100121

Noothi Manisha, Madiraju Jagadeeshwar

引用次数: 0

DEFEAT: A decentralized federated learning against gradient attacks DEFEAT：一种针对梯度攻击的去中心化联合学习

High-Confidence Computing Pub Date : 2023-09-01 DOI: 10.1016/j.hcc.2023.100128

Guangxi Lu , Zuobin Xiong , Ruinian Li , Nael Mohammad , Yingshu Li , Wei Li

{"title":"DEFEAT: A decentralized federated learning against gradient attacks","authors":"Guangxi Lu , Zuobin Xiong , Ruinian Li , Nael Mohammad , Yingshu Li , Wei Li","doi":"10.1016/j.hcc.2023.100128","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100128","url":null,"abstract":"<div><p>As one of the most promising machine learning frameworks emerging in recent years, Federated learning (FL) has received lots of attention. The main idea of centralized FL is to train a global model by aggregating local model parameters and maintain the private data of users locally. However, recent studies have shown that traditional centralized federated learning is vulnerable to various attacks, such as gradient attacks, where a malicious server collects local model gradients and uses them to recover the private data stored on the client. In this paper, we propose a decentralized federated learning against aTtacks (DEFEAT) framework and use it to defend the gradient attack. The decentralized structure adopted by this paper uses a peer-to-peer network to transmit, aggregate, and update local models. In DEFEAT, the participating clients only need to communicate with their single-hop neighbors to learn the global model, in which the model accuracy and communication cost during the training process of DEFEAT are well balanced. Through a series of experiments and detailed case studies on real datasets, we evaluate the excellent model performance of DEFEAT and the privacy preservation capability against gradient attacks.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 3","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50191222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An efficient identity-based signature protocol over lattices for the smart grid 智能电网中一种有效的基于身份的格签名协议

High-Confidence Computing Pub Date : 2023-08-19 DOI: 10.1016/j.hcc.2023.100147

Longzhu Zhu , Fan Jiang , Min Luo , Quanrun Li

引用次数: 0

Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent 基于智能代理强化学习的云边界网络主动防御决策方法研究

High-Confidence Computing Pub Date : 2023-08-11 DOI: 10.1016/j.hcc.2023.100145

Huan Wang , Yunlong Tang , Yan Wang , Ning Wei , Junyi Deng , Zhiyan Bin , Weilong Li

{"title":"Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent","authors":"Huan Wang , Yunlong Tang , Yan Wang , Ning Wei , Junyi Deng , Zhiyan Bin , Weilong Li","doi":"10.1016/j.hcc.2023.100145","DOIUrl":"10.1016/j.hcc.2023.100145","url":null,"abstract":"<div><p>The cloud boundary network environment is characterized by a passive defense strategy, discrete defense actions, and delayed defense feedback in the face of network attacks, ignoring the influence of the external environment on defense decisions, thus resulting in poor defense effectiveness. Therefore, this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent, designs the network structure of the intelligent agent attack and defense game, and depicts the attack and defense game process of cloud boundary network; constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment, and portrays the interaction process between intelligent agent and environment; establishes the reward mechanism based on the attack and defense gain, and encourage intelligent agents to learn more effective defense strategies. the designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics, interaction lag, and control dispersion in the defense decision process of cloud boundary networks, and improve the autonomy and continuity of defense decisions.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 2","pages":"Article 100145"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295223000430/pdfft?md5=3999db4a0c52cf4973ef09d3cfb36ff6&pid=1-s2.0-S2667295223000430-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75345400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

JFinder: A novel architecture for java vulnerability identification based quad self-attention and pre-training mechanism JFinder:一种基于quad自注意和预训练机制的java漏洞识别新架构

High-Confidence Computing Pub Date : 2023-08-09 DOI: 10.1016/j.hcc.2023.100148

Jin Wang , Zishan Huang , Hui Xiao, Yinhao Xiao

引用次数: 0