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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4026-4041页

作者： Zhu, Kun Zhang, Nana Ding, Weiping Jiang, Changjun the Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China the School of Computer Science and Technology Donghua University Shanghai201620 China the School of Information Science and Technology Nantong University Nantong226019 China

Heterogeneous fraud detection is an important means of credit card security assurance, which can utilize historical transaction records in a source and target domain to build an effective fraud detection model. Nevertheless, large feature distribution differences between source and target transaction instances and the complex intrinsic structure hidden behind transaction data make it difficult for existing credit card fraud detection (CCFD) models to capture and transfer the most informative feature representations and seriously hinder detection performance. In this work, we propose a novel adaptive heterogeneous CCFD model named adaptive heterogeneous credit card fraud detection model based on deep reinforcement training subset selection (RTAHC) based on deep reinforcement training subset selection, which mainly contains two components: selection distribution generator (SDG) and transaction fraud detector (TFD, including feature extractor with an attention mechanism and classifier). The SDG can generate the selection probability distribution vector via the reinforcement reward mechanism, and then transaction instances in the source domain relevant to the target domain are selected. The feature extractor with an attention mechanism can learn the abstract deep semantic feature representations of selected source transaction instances and the target domain. The joint training of SDG and TFD can provide more real-time and accurate transaction feature representations to reduce the distribution discrepancy between the two domains. We verify the detection performance of RTAHC across a large real-world credit card transaction dataset and four public datasets. Experimental results demonstrate that the RTAHC model can exhibit competitive CCFD performance. Impact Statement—With the rise of artificial intelligence (AI)generated models, credit card fraud has become increasingly rampant, which also causes tens of billions of U.S. dollars in credit card losses worldwide every year

关键词： Hidden Markov models

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New Construction of Code-Based Signature Schemes 26th

New Construction of Code-Based Signature Schemes

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26th International Conference on information and Communications security, ICICS 2024

作者： Yang, Yang Zhang, Fangguo School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9789819788002

In this paper, we present a novel approach to construct code-based digital signature schemes. Our focus is on addressing the efficiency issues stemming from the low proportion of decodable syndromes in classical CFS signature scheme. To overcome this challenge, we introduce a well-defined syndrome subspace wherein all syndromes can be efficiently decoded. Additionally, our scheme differs from traditional code-based approaches by employing a novel method for constructing public and private keys, replacing permutation matrices with invertible matrices. This transformation yields a public key matrix that is statistically indistinguishable from a random matrix. We demonstrate that our scheme achieves existential unforgeability under an adaptive chosen message attack (EUF-CMA) in the random oracle model. Comparative analysis against other code-based signature schemes, such as Wave and Enhanced pqsigRM, reveals significant advantages in both public key and signature sizes. Furthermore, compared to the three post-quantum signature schemes (Crystals-Dilithium, Falcon, and Sphincs) selected as finalists by NIST PQC, our scheme still maintains a significant advantage in terms of signature size. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quantum cryptography

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Research on the Application of Digital Signature in University Electronic Government System 6

Research on the Application of Digital Signature in Universi...

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6th International Conference on computer network, Electronic and Automation, ICCNEA 2023

作者： Wang, Tianma Zhao, Dongmei Qi, Jianjun North China Electric Power University International Education Institute Beijing China Hebei Normal University College of Computer and Cyber Security Shijiazhuang China Key Laboratory of Network and Information Security in Hebei Province Shijiazhuang China

ISBN: (纸本)9798350305388

This article analyzes the principles and processes of digital signature technology, as well as its application methods in the university e-government system. On the one hand, it meets the business management needs of university, and on the other hand, the application of digital signature and timestamp electronic signature ensures the security of university e-government SystemThe university e-government management system based on digital signature technology verifies the identity of users such as the sender and receiver of files, and issues digital certificates. The sender uses digital certificates for system authorization. Signs and encrypts confidential files such as documents through digital signatures to protecting the security of electronic file transmission. Upload the digitally signed and encrypted documents to the system, which completes the sending and receiving of the documents. On the one hand, the recipient downloads documents through the system and uses digital signatures to verify the integrity and authenticity of the documents. On the other hand, digitally sign the document and upload it to the system. Both parties complete the distribution and upload of documents through the system. © 2023 IEEE.

关键词： Authentication

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TAKAP: A Lightweight Three-Party Authenticated key Agreement Protocol with User Anonymity

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Chinese Journal of Electronics 2021年第4期30卷 686-696页

作者： QIAO Hui DONG Xuewen SHEN Yulong LIU Haozhe YANG Lingxiao School of Computer Science and Technology Xidian University Shaanxi Key Laboratory of Network and System Security Xidian University

The three-party authenticated key agreement protocol is a significant cryptographic mechanism for secure communication, which encourages two entities to authenticate each other and generate a shared session key with the assistance of a trusted party(remote server) via a public channel. Recently, Wang et al. put forward a three-party key agreement protocol with user anonymity and alleged that their protocol is able to resist all kinds of attacks and provide multifarious security features in computer Engineering & Science, No.3, ***, we show that Wang et al.'s protocol is vulnerable to the password guessing attack and fails to satisfy user anonymity and perfect secrecy. To solve the aforementioned problems, a lightweight chaotic mapbased Three-party authenticated key agreement protocol(short for TAKAP) is proposed, which not only could provide privacy protection but also resist a wide variety of security attacks. Furthermore, it is formally proved under Burrows-Abadi-Needham(BAN) logic. Simultaneously,the performance analysis in this paper demonstrates that the proposed TAKAP protocol is more secure and efficient compared with other relevant protocols.

关键词： Three-party Authenticated key agreement Chaotic map Anonymity security attacks

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A universal intelligent method for intrusion detection

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Journal of Cyber security Technology 2022年第1-2期6卷 91-111页

作者： Wang, Yong Xi, Jinsong Zhong, Meiling School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guangxi Guilin China

Machine learning algorithms have been widely used in the field of intrusion detection, which effectively improves the detection effect. However, with changeable attack methods and the increasingly complex data, which may make detection inefficient. Therefore, in this paper, we propose an intrusion detection method which can effectively deal with many kinds of attack scenarios. Firstly, the data is preprocessed by data selection or transformation, numerical, normalization, k-classification, uniqueness and missing data processing, and then the RFSGA algorithm is formed by combining ReliefF, Relative Rough Set (RRS) and genetic algorithm (GA) for attribute reduction. The learning model is constructed by using the improved clustering method, in which we apply genetic algorithm for selecting the optimal super parameters of each cluster. Finally, we use KDDCUP99, CICIDS2017 and ADFA-LD to verify the performance of the proposed method. In terms of accuracy (acc), F1 value (f1) and the corresponding detection time (time), simulation results prove that the proposed method has obvious advantages in comparison with the algorithm before improvement and similar studies. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Genetic algorithms

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Prompting Large Language Models with Knowledge-Injection for Knowledge-Based Visual Question Answering

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Big Data Mining and Analytics 2024年第3期7卷 843-857页

作者： Zhongjian Hu Peng Yang Fengyuan Liu Yuan Meng Xingyu Liu School of Computer Science and Engineering Southeast University the Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education of the People’s Republic of ChinaNanjing 211189China Southeast University-Monash University Joint Graduate School(Suzhou) Southeast UniversitySuzhou 215125China

Previous works employ the Large Language Model(LLM)like GPT-3 for knowledge-based Visual Question Answering(VQA).We argue that the inferential capacity of LLM can be enhanced through knowledge *** methods that utilize knowledge graphs to enhance LLM have been explored in various tasks,they may have some limitations,such as the possibility of not being able to retrieve the required *** this paper,we introduce a novel framework for knowledge-based VQA titled“Prompting Large Language Models with Knowledge-Injection”(PLLMKI).We use vanilla VQA model to inspire the LLM and further enhance the LLM with knowledge *** earlier approaches,we adopt the LLM for knowledge enhancement instead of relying on knowledge ***,we leverage open LLMs,incurring no additional *** comparison to existing baselines,our approach exhibits the accuracy improvement of over 1.3 and 1.7 on two knowledge-based VQA datasets,namely OK-VQA and A-OKVQA,respectively.

关键词： visual question answering knowledge-based visual question answering large language model knowledge injection

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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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Electronic band structures of topological kagome materials

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Chinese Physics B 2025年第1期34卷 29-44页

作者： Man Li Huan Ma Rui Lou Shancai Wang School of Information Network Security People’s Public Security University of ChinaBeijing 100038China Department of Physics Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)and Beijing Key Laboratory of Opto-electronic Functional Materials&Micronano DevicesRenmin University of ChinaBeijing 100872China Leibniz Institute for Solid State and Materials Research IFW Dresden01069 DresdenGermany Helmholtz-Zentrum Berlin fur Materialien und Energie Albert-Einstein-Strabe 1512489 BerlinGermany Joint Laboratory“Functional Quantum Materials”at BESSY II 12489 BerlinGermany

The kagome lattice has garnered significant attention due to its ability to host quantum spin Fermi liquid ***,the combination of unique lattice geometry,electron–electron correlations,and adjustable magnetism in solid kagome materials has led to the discovery of numerous fascinating quantum *** include unconventional superconductivity,charge and spin density waves(CDW/SDW),pair density waves(PDW),and Chern insulator *** emergent states are closely associated with the distinctive characteristics of the kagome lattice's electronic structure,such as van Hove singularities,Dirac fermions,and flat bands,which can exhibit exotic quasi-particle excitations under different symmetries and magnetic ***,various quantum kagome materials have been developed,typically consisting of kagome layers stacked along the z-axis with atoms either filling the geometric centers of the kagome lattice or embedded between the *** this topical review,we begin by introducing the fundamental properties of several kagome *** gain an in-depth understanding of the relationship between topology and correlation,we then discuss the complex phenomena observed in these *** include the simplest kagome metal T_(3)X,kagome intercalation metal T X,and the ternary compounds AT_(6)X_(6)and RT_(3)X_(5)(A=Li,Mg,Ca,or rare earth;T=V,Cr,Mn,Fe,Co,Ni;X=Sn,Ge;R=K,Rb,Cs).Finally,we provide a perspective on future experimental work in this field.

关键词： kagome lattice quasi-particle excitation electronic correlation magnetism

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Vertical Federated Learning Based on Consortium Blockchain for Data Sharing in Mobile Edge Computing

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computer Modeling in Engineering & Sciences 2023年第10期137卷 345-361页

作者： Yonghao Zhang Yongtang Wu Tao Li Hui Zhou Yuling Chen State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and TechnologyWeifang262700China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin541004China

The data in Mobile Edge Computing(MEC)contains tremendousmarket value,and data sharing canmaximize the usefulness of the ***,certain data is quite sensitive,and sharing it directly may violate *** Federated Learning(VFL)is a secure distributed machine learning framework that completes joint model training by passing encryptedmodel parameters rather than raw data,so there is no data privacy leakage during the training ***,the VFL can build a bridge between data demander and owner to realize data sharing while protecting data ***,the VFL requires a third party for key distribution and decryption of training *** this article,we employ the consortium blockchain instead of the traditional third party and design a VFL architecture based on the consortium blockchain for data sharing in *** specifically,we propose a V-Raft consensus algorithm based on Verifiable Random Functions(VRFs),which is a variant of the *** VRaft is able to elect leader quickly and stably to assist data demander and owner to complete data sharing by ***,we apply secret sharing todistribute the private key to avoid the situationwhere the training result cannot be decrypted if the leader ***,we analyzed the performance of the V-Raft and carried out simulation experiments,and the results show that compared with Raft,the V-Raft has higher efficiency and better scalability.

关键词： Mobile edge computing vertical federated learning consortium blockchain consensus algorithm

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4th International Conference on computer Science and Blockchain, CCSB 2024

作者： Huang, Guimin Shi, Canjun School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350356502

Short textual similarity has been more and more widely used in the field of natural language processing. However, people tend to pay more attention to the semantic similarity of sentences and ignore the word order similarity in sentences. For this reason, we propose a short text similarity computation model that fuses semantics and word order. In terms of semantics, we propose a adjacency-aware similarity model based on deep convolutional neural networks, which effectively exploits the adjacency relationship in sentences and extracts text features from multiple perspectives for similarity prediction. In terms of word order, a word order model incorporating an external knowledge base is proposed, which uses an external knowledge base with pointwise mutual information to construct word order dense vectors to assess word order similarity. Ultimately, we propose a comprehensive assessment model for short text similarity that incorporates semantic and word order information. We conducted experiments on the public dataset MRPC, and the experimental results show that our model outperforms existing models in terms of performance. © 2024 IEEE.

关键词： Convolutional neural networks

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