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arXiv 2023年

作者： Pan, Zhiyuan Hu, Xing Xia, Xin Zhan, Xian Lo, David Yang, Xiaohu The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China The Hong Kong Polytechnic University Hong Kong Hong Kong School of Computing and Information Systems Singapore Management University Singapore

The number of vulnerabilities reported in open source software has increased substantially in recent years. security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4J (Patch Presence Test for Java Binaries). Ppt4J is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4J, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4J achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4J on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor. © 2023, CC BY-NC-ND.

关键词： Open source software

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Attribute-Based key Management for Patient-Centric and Trusted data Access in blockchain-Enabled IoMT

SSRN

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SSRN 2023年

作者： Xiang, Axin Gao, Hongfeng Tian, Youliang Wang, Linjie Xiong, Jinbo State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guizhou University Guizhou Guiyang550025 China Network and Information Management Center Guizhou University Guizhou Guiyang550025 China School of Data Science Tongren University Guizhou Tongren554300 China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350117 China

Internet of Medical Things (IoMT) provides real-time data collection and access services to medical entities by connecting their devices to Internet. However, within such a complex architecture, achieving patient-centric data access still presents trustworthiness challenges. Ciphertext-policy attribute-based encryption (CP-ABE) and blockchain are both the promising cryptographic technologies that support patients with secure and fine-grained access control over their data. Nevertheless, both have their unique key management issues. In this paper, we propose a novel patient-centric and trusted data access (PCT-DA) system in blockchain-enabled IoMT. Firstly, we develop an online/offline encryption and verifiable outsourced decryption CP-ABE scheme to relieves the computation pressure on patients/users. Secondly, we design a flexible attribute-based key management mechanism to overcome the security drawbacks of data encryption keys and user attribute keys. Finally, we build a trusted integrity audit contract to guarantee the incentive fairness of patients/users, allowing them to release/access medical data honestly. Moreover, theoretical analyses prove that the PCT-DA system not only achieves confidentiality, integrity, auditability and forward/backward security, but also guarantees static security under random oracles. Simulation experiments indicate that our proposal reduces the time cost by approximately 14.14 seconds over state-of-the-art and is more flexible in BE-IoMT. © 2023, The Authors. All rights reserved.

关键词： blockchain

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Q-Learning-Based Energy-Efficient Routing Protocol for Underwater Wireless Sensor Networks

Q-Learning-Based Energy-Efficient Routing Protocol for Under...

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IEEE/OES China Ocean Acoustics (COA)

作者： Xuerui Liu Feng Zhou National Key Laboratory of Underwater Acoustic Technology Harbin Engineering University Harbin China Key Laboratory of Marine Information Acquisition and Security Ministry of Industry and Information Technology Harbin Engineering University Harbin China College of Underwater Acoustic Engineering Harbin Engineering University Harbin China Sanya Nanhai Innovation and Development Base of Harbin Engineering University Sanya China

ISBN: (数字)9798350340297

ISBN: (纸本)9798350340303

Underwater wireless sensor networks (UWSNs) have garnered considerable attention in recent years. Subject to constraints such as fixed node locations, limited energy, and harsh communication environments, designing high-performance routing protocols becomes crucial. Herein, we propose a Q-learning-based energy-efficient routing (QEER) protocol for UWSNs. To be specific, the reward function of Q-learning is designed by incorporating the energy, distance, and redundancy factors. Different from existing works ignoring redundant nodes in routing design, we exploit redundant nodes as relay nodes to extend the lifetime of UWSNs. In addition, the Time Division Multiple Access and Code Division Multiple Access protocols are reasonably implemented. Simulations demonstrate that compared with conventional clustering algorithms, the proposed QEER protocol achieves substantial improvements in terms of node lifetime, energy consumption, and the amount of forwarded data.

关键词： Wireless sensor networks Time division multiple access Energy consumption Q-learning Redundancy Routing Routing protocols Energy efficiency Topology Relays

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A MAC Protocol Identification Approach Based on Convolutional Neural Network

A MAC Protocol Identification Approach Based on Convolutiona...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Xutong Zhang Weiguo Shen Jianliang Xu Zitong Liu Guoru Ding Colledge of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC Jiaxing China College of Communication Engineering Army Engineering University Nanjing China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

The medium access control (MAC) protocol identification is of great application value in cognitive radio. In order to realize the MAC protocol identification with high accuracy and avoid manual feature extraction, we convert the sampled data into the form of spectrogram. Then, a graphical scheme which combines the convolutional neural network (CNN) and the spectrogram is proposed. The simulation includes CNN method and support vector machine (SVM) method. It is suggested that the CNN method has better identification performance.

关键词： Media Access Protocol Spectrogram Protocols Support vector machines Time division multiple access Feature extraction Cognitive radio

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Sustainability Forecasting for Deep Learning Packages

Sustainability Forecasting for Deep Learning Packages

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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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