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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Junxiao Han Yunkun Wang Zhongxin Liu Lingfeng Bao Jiakun Liu David Lo Shuiguang Deng School of Computer & Computing Science Hangzhou City University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computing and Information Systems Singapore Management University Singapore Singapore

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL packages acting as the bridge nodes, and downstream DL projects acting as the periphery. However, most Open Source Software (OSS) projects may fail. Considering the crucial position of DL packages in the DL SC, to foster the sustainable development of DL SCs and DL packages, we aim to forecast the long-term sustainability of DL packages. Here, sustained activity is adopted as the main proxy of sustainability, and the sustainability status is classified as “sus-tainable” or “dormant”. Relatedly, a DL package is considered as “sustainable” if it has sustained activity in its last 12 months. Otherwise, it is deemed as “dormant”. To this end, we propose an approach that begins with obtaining longitudinal features for each DL package in each month. Then, we develop a model to forecast the sustainability of DL packages by incorporating the longitudinal features, which can aptly predict sustainability with an accuracy of up to 0.81. Subsequently, an interpretable module is developed to interpret the determinants (i.e., important features) that impact the sustainability of DL packages. Finally, we generate sustainability trajectories for each DL package to better understand the monthly changes of their sustainability status. Our findings uncover that for most DL packages, fewer but more centralized developers and a balanced collaboration are more likely to help sustain the DL packages. Furthermore, although some DL packages are sustainable, their sustainability trajectories present statistically decreasing trends over time. Based on the findings, we shed light on the dynamic sustainability of DL packages, highlight future research directions, and provide practical suggestions to DL package maintainers, developers, users, and software engineering researchers.

关键词： Deep learning Supply chains Predictive models Market research Trajectory Sustainable development Task analysis

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Measurement-based analysis of characteristics of fast moving underwater acoustic communication channel

Measurement-based analysis of characteristics of fast moving...

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Frontiers of information technology (FIT)

作者： Yang Wang Honglu Yan Chenyu Pan Songzuo Liu State Key Laboratory of Acoustics Chinese Academy of Sciences Beijing China Maritime Environment Construction Office Beijing China Acoustic Science and Technology Laboratory Harbin Engineering University Harbin China Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University) Ministry of Industry and Information Technology Harbin China College of Underwater Acoustic Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798350345940

In the fast moving scene, the underwater acoustic communication (UWAC) channel has extremely complex time-varying characteristics. The lack of standardized channel model severely limits the development of mobile UWAC technology. In this paper, the channel time-varying impulse response (TVIR) is estimated based on the measured mobile UWAC data, and the correlation coefficient, spread function and envelope of the channel are analyzed. The analysis results show that the fast movement between the transmitter and the receiver will lead to serious deviation and amplitude variation of the multipath arrival time, which will cause large Doppler frequency offset and spread, and lead to the decline of channel correlation. In this paper, the statistical characteristics of the measured channel are analyzed, which is helpful to the development of mobile UWA channel model.

关键词： Time-frequency analysis Transmitters Channel estimation Receivers Acoustic measurements Channel models Velocity measurement

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AUV-Aided Deep-Sea Internet of Things: A Machine Learning Perspective

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IEEE Network 2024年

作者： Men, Wei Wang, Jingjing Li, Zonglin Mi, Weishi Yin, Jingwei Ren, Yong Tsinghua University Department of Electronic Engineering Beijing100084 China Beihang University School of Cyber Science and Technology Beijing100191 China Beihang University Hangzhou Innovation Institute Hangzhou310051 China Tsinghua University Department of Tsinghua Shenzhen International Graduate School Shenzhen518055 China Key Laboratory for Polar Acoustics and Application Ministry of Education Harbin150001 China National Key Laboratory of Underwater Acoustic Technology Harbin150001 China Harbin Engineering University College of Underwater Acoustic Engineering Key Laboratory of Marine Information Acquisition and Security Ministry of Industry and Information Technology Harbin150001 China

The deep-sea Internet of Things (DS-IoT) enhances massive data acquisition capabilities and augments the safety of industrial operations, serving as a powerful technology for exploiting rich and precious natural resources. However, the unique physical environment of the deep sea poses significant challenges in establishing a DS-IoT network. In this article, to improve the capabilities of deep-sea information collection, transmission, and processing, we conceive a six-layer system architecture for the future DS-IoT, which consists of layers for environment sensing, information collection, long-distance information transmission, control and information aggregation, fusion and processing, and application and service. Then, considering the complexity of the deep-sea environment that harms the stability of underwater networks, traditional networking techniques will falter, which motivates this article to propose machine learning-aided key techniques, focusing on communication, sensing, processing, and network aspects. Furthermore, we discuss the research on efficient trajectory planning and task scheduling for multi-autonomous underwater vehicles collaboration. The simulation results indicate that our method achieves reductions of 66.84% and 89.23% in scheduling route length and reasoning time, respectively, compared to traditional methods. Finally, we present the challenges faced by the DS-IoT and potential research trends. © 1986-2012 IEEE.

关键词： Underwater acoustics

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DE-RSTC: A rational secure two-party computation protocol based on direction entropy

arXiv

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arXiv 2021年

作者： Chen, Yuling Ma, Juan Wang, Xianmin Zhang, Xinyu Zhou, Huiyu State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University Of Electronic Technology Guilin541000 China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510000 China University of Leicester LeicesterLe1 7RH United Kingdom

Rational secure multi-party computation (RSMC) means two or more rational parties to complete a function on private inputs. In the process, the rational parties choose strategies to maximize utility, which will cause players to maliciously execute the protocol and undermine the fairness and correctness of the protocol. To solve this problem, we leverage game theory to propose the direction entropy-based solution. First, we utilize the direction vector of the direction entropy to examine the player's strategy uncertainty and quantify its strategy from different dimensions. Specifically, when parties choose a cooperation strategy, the direction vector is positive, and the information transmitted is positive, conversely, it is negative information. Then, we provide mutual information to construct new utility functions for the players. What's more, we measure the mutual information of players to appraise their strategies. Finally, we prove in detail the protocol we gave, and the result show that the fairness problem in rational secure two-party computation. We also prove that the proposed protocol reaches the Nash equilibrium. Furthermore, we conduct experiments using mutual information to construct utility, and the results show that the utility obtained when the player is honest will be higher. Copyright © 2021, The Authors. All rights reserved.

关键词： Entropy

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Mechanical Vibration Signal Compression Based on Speech Codecs for Intelligent Manufacturing

SSRN

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SSRN 2023年

作者： Tang, Lili Tian, Hui Chang, Chin-Chen Huang, Hui College of Mechanical Engineering and Automation Huaqiao University Xiamen361021 China College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen361021 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

How to effectively compress mechanical signals so that they can support remote and real-time health monitoring is a hot issue in the context of intelligent manufacturing. Therefore, this paper presents a novel mechanical vibration signal compression scheme based on speech codecs, which can realize signal compression in many scenarios such as tool wear detection and bearing fault diagnosis. In this work, signal compression is first transformed into a multi-objective optimization problem, and the feasibility of solving this problem from the perspective of speech coding is deeply analyzed. Further, this paper presents five compression algorithms with different compression ratios. The presented algorithms are evaluated with tool wear detection in stone processing and bearing fault diagnosis. The experimental results show that the proposed algorithms can achieve higher compression ratios (4.0~24.6) while ensuring low time overhead (average time not more than 0.0194ms) and high reconstruction quality (MSE index are all lower than 0.008). © 2023, The Authors. All rights reserved.

关键词： Fault detection

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Ghost-in-Wave: How Speaker-Irrelative Features Interfere DeepFake Voice Detectors

Ghost-in-Wave: How Speaker-Irrelative Features Interfere Dee...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xuan Hai Xin Liu Zhaorun Chen Yuan Tan Song Li Weina Niu Gang Liu Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou University Lanzhou China Elmore Family School of Electrical and Computer Engineering Purdue University IN USA The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Recent speech synthesis technology can generate high-quality speech indistinguishable from human speech, thus introducing various security and privacy risks. Numerous recent studies have focused on fake voice detection to address these risks, with many claiming to achieve ideal performance. However, is this really the case? A recent research work introduced Speaker-Irrelative-Features (SiFs), unrelated to the information in speech files but capable of influencing fake detectors. This means that existing detectors may rely on SiFs to a certain extent to distinguish real and fake speech. In this paper, we introduce an evaluation framework to evaluate the influence of SiFs in existing fake voice detectors in depth. We evaluate three SiFs which include background noise, the mute parts before and after voice, and the sampling rate on ASVspoof2019 and FoR. Our results confirm the substantial influence of SiFs on fake voice detection performance, and we delve into the analysis of the underlying mechanisms.

关键词： Privacy Deepfakes Detectors Feature extraction Background noise Speech synthesis security

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Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature

Decentralized and Lightweight Cross-Chain Transaction Scheme...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Huiying Zou Jia Duan Xi Liu Wei Ren Tao Li Xianghan Zheng Kim-Kwang Raymond Choo School of Computer Science China University of Geosciences Wuhan China The Third Surveying and Mapping Institute of Hunan Province Hunan Engineering Research Center of Geographic Information Security and Application Hunan China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China College of Computer and Big Data Fuzhou University School of Information and Intelligent Engineering Sanya College China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach’s security.

关键词： Privacy Systematics Costs Scalability blockchains security

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A Novel Lexicon Enhanced Named Entity Recognition Model for Network security Domain

A Novel Lexicon Enhanced Named Entity Recognition Model for ...

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Chinese Automation Congress (CAC)

作者： Zhiyang Jin Yusun Fu Yuehui Huan Jinliang Xue Chengguang Wang Yuanfang Chen Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Zhejiang Guoli Security Technology Co. Ltd Ningbo China Ningbo Industrial Internet Institute Ningbo China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Named Entity Recognition (NER) technology serves as the foundation for solving many Natural Language Processing(NLP) problems, particularly in the domain of building knowledge graphs, where an effective NER model is crucial for creating efficient knowledge representations. However, when constructing knowledge graphs in the Industrial Internet security(IIS) domain, security-related incidents, as significant entities, pose challenges for existing NER models. In this paper, we propose security Lexicon Enhanced Bidirectional Encoder Representations from Transformers(SEBERT). SEBERT enhances semantic processing by integrating security lexicon information into one transformer layer of BERT, assigning vocabulary to each character in input sentences. Then we use a Residual-Knowledge-Arnold Network(RKAN) to optimize the semantic features learned from SEBERT, utilizing the properties of residual networks to learn identity mapping features without significantly increasing computational complexity. Finally, the Conditional Random Field (CRF) is used to learn the sequence information and dependencies of adjacent labels. Experimental results demonstrate that SEBERT-RKAN-CRF model achieves superior performance in recognizing entities in the IIS domain, with improvements of 1.99%, 3.34%, and 2.66% in accuracy rate, recall rate, and F1-score compared to the baseline model.

关键词： Vocabulary Semantics Named entity recognition Knowledge graphs Bidirectional control Transformers Feature extraction Conditional random fields Encoding security

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Ppdrm: Privacy-Preserving Drm Training and Classification on the Cloud

SSRN

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SSRN 2023年

作者： Xu, Lida Tian, Chengliang Zhang, Guoyan Li, Leibo Tian, Weizhong Zhang, Yan College of Computer Science and Technology Qingdao University Qingdao266071 China School of Cyber Science and Technology Shandong University Shandong Ji’nan250101 China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong Ji’nan250101 China Shandong Institute of Blockchain Shandong Ji’nan250101 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen518118 China College of Electromechanical Engineering Qingdao University of Science and Technology Qingdao266061 China

The prevalence of cloud computing greatly promotes the development of artificial intelligence(AI). Accurately and efficiently classify data on the cloud is a classic AI task. The recent Discriminative Ridge Machine(DRM) is a new type of supervised classifier which outperforms many other classic classifiers in accuracy and efficiency, especially for high-dimensional data or unbalanced ***, the DRM training and classification task is tedious for resource-constrained data owners. Motivated by this, in this paper, we study the outsourcing of DRM training and classification within a cloud environment and propose a quantum secure and efficient solution for this problem. Specifically, based on the two non-colluding servers model, we adopt the quantum-immune CKKS cryptosystem to design a series of homomorphic security protocols for matrix and argmin operations and realize the correct model training and data classification without leaking the privacy of training dataset, the DRM model, the query data and the classification results. We argue the $\mathcal{CPA}$ secure of our design and evaluate the accuracy and efficiency from theory and experiment perspectives. Our theoretical and experimental analysis demonstrates that our design can achieve the same classification accuracy as the origin plaintext-based DRM classifier and the query-response time is reasonable and acceptable. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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AsyncSC: An Asynchronous Sidechain for Multi-Domain data Exchange in Internet of Things

arXiv

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arXiv 2024年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Gao, Sheng Tong, Wei Lu, Di Shen, Yulong Du, Xiaojiang The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'An710071 China The Zhejiang Lab Hangzhou311121 China The School of Information Central University of Finance and Economics Beijing100081 China The School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China The School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi'An710071 China The School of Engineering and Science Stevens Institute of Technology Hoboken07030 United States

Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) schemes for IoT multi-domain data exchange are constrained by the need for synchronous networks, hindering efficient cross-chain interactions in discontinuous networks and leading to suboptimal data exchange. In this paper, we propose AsyncSC, a novel asynchronous sidechain construction. It employs a committee to provide Cross-blockchain as a Service (C-BaaS) for data exchange in multi-domain IoT. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We analyze and prove the security of AsyncSC, simulate an asynchronous communication environment, and conduct a comprehensive evaluation. The results show that AsyncSC outperforms SOTA schemes, improving throughput by an average of 1.21 to 3.96 times, reducing transaction latency by 59.76% to 83.61%, and maintaining comparable resource overhead. Copyright © 2024, The Authors. All rights reserved.

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