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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Luo, Xudong Luo, Ying Yang, Binxia School of Computer Science and Engineering Guangxi Normal University Guilin541004 China Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China

ISBN: (纸本)9783031578076

Although Pre-trained Language Model (PLM) ChatGPT as a Question-Answering System (QAS) is so successful, it is still necessary to study further the QASs based on PLMs. In this paper, we survey state-of-the-art systems of this kind, identify the issues that current researchers are concerned about, explore various PLM-based methods for addressing them, and compare their pros and cons. We also discuss the datasets used for fine-tuning the corresponding PLMs and evaluating these PLM-based methods. Moreover, we summarise the criteria for evaluating these methods and compare their performance against these criteria. Finally, based on our analysis of the state-of-the-art PLM-based methods for QA, we identify some challenges for future research. © IFIP International Federation for Information Processing 2024.

关键词： Computational linguistics

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Compound Jamming Detection Technique under High Similarity Background

Compound Jamming Detection Technique under High Similarity B...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2024

作者： Li, Zhaobin Zhang, Jiaxiang Liu, Quanhua Zhao, Sanyuan Radar Research Lab Department of Computer Science & Technology Beijing Institute of Technology Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Institute of Technology Beijing China Beijing Institute of Technology Ministry of Education Beijing Institute of Technology Chongqing Innovation Center Chongqing Beijing China Department of Computer Science & Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350353464

To address the issue of easy misidentification due to the similarity between suppression jamming and background noise on the time-frequency graph in radar jamming detection, this paper proposes a signal amplitude detection method based on empirically preset thresholds to distinguish them. To address the problem of misidentifying dense false targets and target signals caused by their identical mathematical models and similar features in the time-frequency graph—both appearing as linear shapes with the same tilt angle—we distinguish them by utilizing the evenly spaced distribution pattern of the dense false targets. The simulation results demonstrate that in the compound scenario of suppression jamming and dense false targets jamming, the YOLOv8 model achieves an F1-score of 95.7% for jamming detection. By integrating the outlier rejection module proposed in this paper, this performance can be enhanced to 98.6%. ©2024 IEEE.

关键词： Jamming

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CPAKE: Dynamic Batch Authenticated Key Exchange with Conditional Privacy 24th

CPAKE: Dynamic Batch Authenticated Key Exchange with Condit...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xiang, Axin Tian, Youliang Xiong, Jinbo Ying, Zuobin Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guiyang550025 China Industrial School of Joint Innovation Quanzhou Vocational and Technical University Quanzhou362268 China Faculty of Data Science City University of Macau 999078 China

ISBN: (纸本)9789819615247

Authenticated key exchange (AKE) needs to be designed for realizing point-to-multipoint secure communications in blockchain networks (BNet). However, since BNet is open, untrusted and decentralized, traditional certificateless AKE schemes are difficult to overcome two technical bottlenecks, i.e., private distribution of partial keys and accurate identification of anonymous traitors. This paper proposes a conditionally private and dynamic batch AKE scheme (CPAKE) in BNet. We first develop a privacy-preserving certificateless key generation algorithm to prevent the disclosure of nodes’ privacy during partial key distribution. Then, we design a simple anonymous traitor tracing mechanism against Byzantine adversaries to accurately identify and trace the traitor’s identity. Finally, we build a dynamic batch authentication protocol with decentralized node management to ensure the final validity of honest nodes. Moreover, performance analysis indicates that our scheme reduces the signing and batch-verifying time by approximately 193.10 ms and 10.93 ms respectively over state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Automatic Classification and Recognition of Seismic Waveforms Based on Convolutional Neural Networks 2

Automatic Classification and Recognition of Seismic Waveform...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Shan, Juhao Huang, Hanming Ministry of Education Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security China Guangxi Normal University School of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350372892

The development of efficient, high-precision, and universally applicable automatic waveform picking algorithms is becoming increasingly important in the era of seismic big data. The main challenge of waveform automatic picking algorithm lies in how to adapt to the classification and screening of different types of seismic events in different regions. Traditional seismic phase identification relies on the theoretical knowledge and experience of relevant researchers, which has problems such as large workload, long interpretation time, strong subjectivity, and low efficiency. Deep learning technology can learn the hidden features in data. This article studies how to use deep convolutional neural networks to learn the nonlinear mapping relationship between seismic data and seismic phases, providing technical support for achieving automated seismic phase recognition. This article mainly focuses on the automatic classification and recognition of seismic waveforms using convolutional neural networks. Firstly, relevant research is summarized, relevant application methods are proposed, and the results are discussed to provide assistance for the development of related fields. © 2024 IEEE.

关键词： Convolutional neural networks

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A Blind Batch Encryption and Public Ledger-Based Protocol for Sharing Sensitive Data

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China Communications 2024年第1期21卷 310-322页

作者： Zhiwei Wang Nianhua Yang Qingqing Chen Wei Shen Zhiying Zhang School of Computer Sciences Nanjing University of Posts and TelecommunicationsNanjing 210023China Dept.of Intelligent Science and Information Law East China University of Political Science and LawShanghai 201620China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal UniversityHaikou 571158China State Key Laboratory of Public Big Data Guizhou UniversityGuizhou 550025China Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Research Institute of Beijing University of Aeronautics and AstronauticsKunming 650233

For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be *** owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the *** prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file *** also provide security analysis in the context of four typical attacks.

关键词： blind batch encryption data sharing onetime adaptive access public ledger security and privacy

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TMAS:A transaction misbehavior analysis scheme for blockchain

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blockchain(Research and Applications) 2024年第3期5卷 42-52页

作者： Shiyong Huang Xiaohan Hao Yani Sun Chenhuang Wu Huimin Li Wei Ren Kim-Kwang Raymond Choo School of Computer Science China University of GeosciencesWuhan430074China Artificial Intelligence Thrust Information HubHong Kong University of Science and TechnologyGuangzhou511400China Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Institute of Beihang UniversityKunming650233China Fujian Key Laboratory of Financial Information Processing Putian UniversityPutian351100China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX 78249-0631USA

blockchain-based cryptocurrencies,such as Bitcoins,are increasingly ***,the decentralized and anonymous nature of these currencies can also be(ab)used for nefarious activities such as money laundering,thus reinforcing the importance of designing tools to effectively detect malicious transaction *** this paper,we propose TMAS,a transaction misbehavior analysis scheme for blockchain-based ***,the proposed system includes ten features in the transaction graph,two heuristic money laundering models,and an analysis method for account linkage,which identifies accounts that are distinct but controlled by an identical *** evaluate the effectiveness of our proposed indicators and models,we analyze 100 million transactions and compute transaction features,and are able to identify a number of suspicious ***,the proposed methods can be applied to other cryptocurrencies,such as token-based cryptocurrencies(e.g.,Bitcoins)and account-based cryptocurrencies(e.g.,Ethereum).

关键词： blockchain Anti-money laundering Bitcoin Cyptocurrency

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Expelliarmus: Command Cancellation Attacks on Smartphones using Electromagnetic Interference 42

Expelliarmus: Command Cancellation Attacks on Smartphones us...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Gao, Ming Xiao, Fu Liu, Weiran Guo, Wentao Huang, Yangtao Liu, Yajie Han, Jinsong Zhejiang University School of Cyber Science and Technology China ZJU-Hangzhou Global Scientific and Technological Innovation Center China Nanjing University of Posts and Telecommunications School of Computer Science China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks China Zhejiang Provincial Key Laboratory of Blockchain and Cyberspace Governance China

ISBN: (纸本)9798350334142

Human-machine interactions (HMIs), e.g., touchscreens, are essential for users to interact with mobile devices. They are also beneficial in resisting emerging active attacks, which aim at maliciously controlling mobile devices, e.g., smartphones and tablets. With touchscreen-like HMIs, users can notice and interrupt malicious actions conducted by the attackers timely and perform necessary countermeasures, e.g., tapping the 'Quit' button on the touchscreen. However, the effect of HMI-oriented active attacks has not been investigated yet. In this paper, we present a practical attack towards touch-based devices, namely Expelliarmus. It reveals a new attack surface of active attacks for hijacking users' operations and thus taking full control over victim devices. Expelliarmus neutralizes users' touch commands by producing a reverse current via electromagnetic interference (EMI). Since the reverse current offsets the current change caused by a touch, the touchscreen detects no current change and thus ignores users' commands. Besides this basic denial-of-service attack, we also realize a target cancellation attack, which can neutralize target commands, e.g., 'Quit' without interference in irrelevant operations. Thus, the active attack can be completely performed without interruption from users, even if they are alerted by the abnormal events. Extensive evaluations demonstrate the effectiveness of Expelliarmus on 29 off-the-shelf devices. © 2023 IEEE.

关键词： Signal interference

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Impact-aware Maneuver Decision with Enhanced Perception for Autonomous Vehicle 39

Impact-aware Maneuver Decision with Enhanced Perception for ...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Liu, Shuncheng Xia, Yuyang Chen, Xu Xie, Jiandong Su, Han Zheng, Kai University of Electronic Science and Technology of China School of Computer Science and Engineering China Huawei Cloud Database Innovation Lab China China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study China

ISBN: (纸本)9798350322279

Autonomous driving is an emerging technology that has developed rapidly over the last decade. There have been numerous interdisciplinary challenges imposed on the current transportation system by autonomous vehicles. In this paper, we conduct an algorithmic study on the autonomous vehicle decision-making process, which is a fundamental problem in the vehicle automation field and the root cause of most traffic congestion. We propose a perception-and-decision framework, called HEAD, which consists of an enHanced pErception module and a mAneuver Decision module. HEAD aims to enable the autonomous vehicle to perform safe, efficient, and comfortable maneuvers with minimal impact on other vehicles. In the enhanced perception module, a graph-based state prediction model with a strategy of phantom vehicle construction is proposed to predict the one-step future states for multiple surrounding vehicles in parallel, which deals with sensor limitations such as limited detection range and poor detection accuracy under occlusions. Then in the maneuver decision module, a deep reinforcement learning-based model is designed to learn a policy for the autonomous vehicle to perform maneuvers in continuous action space w.r.t. a parameterized action Markov decision process. A hybrid reward function takes into account aspects of safety, efficiency, comfort, and impact to guide the autonomous vehicle to make optimal maneuver decisions. Extensive experiments offer evidence that HEAD can advance the state of the art in terms of both macroscopic and microscopic effectiveness. © 2023 IEEE.

关键词： Autonomous vehicles

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FedISMH: Federated Learning Via Inference Similarity for Model Heterogeneous

FedISMH: Federated Learning Via Inference Similarity for Mod...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Li, Yongdong Wang, Li-E Sun, Zhigang Li, Xianxian Chang, Hengtong Xu, Jinke Lin, Caiyi Guangxi Normal University School of Computer Science and Engineering Guilin China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin China

ISBN: (纸本)9798350362480

Federated Learning (FL) is a privacy-preserving machine learning paradigm, enabling decentralized devices to collaboratively train models without sharing local data. Traditional FL approaches, however, rely on averaging parameters across clients with homogeneous models, which limits their applicability in scenarios where clients require heterogeneous models. In this paper, we propose FedISMH, a novel approach to address model heterogeneity in FL. Instead of directly applying knowledge distillation, FedISMH clusters clients based on the structural similarities of client models, where clients' structural features can be extracted through either labeled or unlabeled dataset This allows the proposed model to identify clients with similar model architectures while preserving privacy. Additionally, FedISMH introduces a dynamic mechanism to manage noise clients by aligning them with the most structurally similar clusters, ensuring that their inclusion promote the performance of the cluster. Experimental results on MNIST and SVHN demonstrate that FedISMH consistently outperforms state-of-the-art methods in both IID and Non-IID settings, offering improved accuracy, robustness, and flexibility in heterogeneous FL environments. © 2024 IEEE.

关键词： Federated learning

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Smart Energy Management System Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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