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Attention-Enhanced Voice Portrait Model Using Generative Adversarial network

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Computers, Materials & Continua 2024年第4期79卷 837-855页

作者： Jingyi Mao Yuchen Zhou YifanWang Junyu Li Ziqing Liu Fanliang Bu School of Information network security People’s Public Security University of ChinaBeijing100038China

Voice portrait technology has explored and established the relationship between speakers’ voices and their facialfeatures, aiming to generate corresponding facial characteristics by providing the voice of an unknown *** to its powerful advantages in image generation, Generative Adversarial networks (GANs) have now beenwidely applied across various fields. The existing Voice2Face methods for voice portraits are primarily based onGANs trained on voice-face paired datasets. However, voice portrait models solely constructed on GANs facelimitations in image generation quality and struggle to maintain facial similarity. Additionally, the training processis relatively unstable, thereby affecting the overall generative performance of the model. To overcome the abovechallenges,wepropose a novel deepGenerativeAdversarialnetworkmodel for audio-visual synthesis, namedAVPGAN(Attention-enhanced Voice Portrait Model using Generative Adversarial network). This model is based ona convolutional attention mechanism and is capable of generating corresponding facial images from the voice ofan unknown speaker. Firstly, to address the issue of training instability, we integrate convolutional neural networkswith deep GANs. In the network architecture, we apply spectral normalization to constrain the variation of thediscriminator, preventing issues such as mode collapse. Secondly, to enhance the model’s ability to extract relevantfeatures between the two modalities, we propose a voice portrait model based on convolutional attention. Thismodel learns the mapping relationship between voice and facial features in a common space from both channeland spatial dimensions independently. Thirdly, to enhance the quality of generated faces, we have incorporated adegradation removal module and utilized pretrained facial GANs as facial priors to repair and enhance the clarityof the generated facial images. Experimental results demonstrate that our AVP-GAN achieved a cosine similarity of0.511, ou

关键词： Cross-modal generation GANs voice portrait technology face synthesis

Generic attacks on small-state stream cipher constructions in the multi-user setting

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Cybersecurity 2024年第3期7卷 69-79页

作者： Jianfu Huang Ye Luo Qinggan Fu Yincen Chen Chao Wang Ling Song College of Cyber security Jinan UniversityGuangzhou 510632China National Joint Engineering Research center of network security Detection and Protection Technology Jinan UniversityGuangzhou 510632China

Small-state stream ciphers(SSCs),which violate the principle that the state size should exceed the key size by a factor of two,still demonstrate robust security properties while maintaining a lightweight *** ciphers can be clas-sifed into several constructions and their basic security requirement is to resist generic attacks,ie.,the time-mem-ory-data tradeoff(TMDTO)*** this paper,we investigate the security of small-state constructions in the multi-user *** on it,the TMDTO distinguishing attack and the TMDTO key recovery attack are developed for such a *** is shown that SSCs which continuously use the key can not resist the TMDTO distinguishing ***,SSCs based on the continuous-IV-key-use construction cannot withstand the TMDTO key recovery attack when the key length is shorter than the IV length,no matter whether the keystream length is limited or ***,We apply these two generic attacks to TinyJAMBU and DRACO in the multi-user *** TMDTO distinguish-ing attack on TinyJAMBU with a 128-bit key can be mounted with time,memory,and data complexities of 264,248,and 232,*** attack is comparable with a recent work on ToSC 2022,where partial key bits of TinyJAMBU are recovered with more than 250 users(or keys).As DRACO's IV length is smaller than its key length,itis vulnerable to the TMDTO key recovery *** resulting attack has a time and memory complexity of both 2112,which means DRACO does not provide 128-bit security in the multi-user setting.

关键词： Small-state stream ciphers TMDTO attacks Multi-user setting

Fault-tolerant identity-based encryption from SM9

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Science China(Information Sciences) 2024年第2期67卷 104-117页

作者： Xiaohong LIU Xinyi HUANG Zhaohui CHENG Wei WU Fujian Provincial Key Laboratory of network security and Cryptology College of Computer and Cyber SecurityFujian Normal University Artificial Intelligence Thrust Information HubThe Hong Kong University of Science and Technology (Guangzhou) Olym Information security Technology Ltd. center for Applied Mathematics of Fujian Province School of Mathematics and StatisticsFujian Normal University

This paper initiates the formal study of attribute-based encryption within the framework of SM9,the Chinese National Cryptography Standard for Identity-Based Cryptography, by presenting two new faulttolerant identity-based encryption(FIBE) schemes. Our first scheme uses the same private-key/ciphertext structure as the original SM9 algorithm and operates in a small attribute universe. As a result, it can be effectively and smoothly integrated into the information systems using SM9. In the random oracle model,we prove that our scheme is ciphertext-indistinguishable against fuzzy selective-identity and chosen-plaintext attacks under the(k + 3)-DBDHI assumption. Our second design is a large universe FIBE scheme based on SM9 that is ciphertext-indistinguishable against chosen-plaintext attacks in the random oracle model under the(f, g)-GDDHE assumption. Finally, we compare the communication and computing costs of our schemes to those of other classical ones. The comparison shows that our schemes have comparable performance as others. We believe that our findings will accelerate the applications of SM9 in modern information systems such as cloud computing and blockchain.

关键词： attribute-based encryption identity-based encryption fault-tolerant SM9

Survey on Fake Information Generation,Dissemination and Detection

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Chinese Journal of Electronics 2024年第3期33卷 573-583页

作者： Wanqiu CUI Dawei WANG Na HAN School of National security People's Public Security University of China Big Data center of The Ministry of Public security

The current booming development of the Internet has put the public in an era of information overload,in which false information is mixed and spread *** phenomenon has seriously disturbed the social network ***,a substantial amount of research is beginning to be devoted to the effective management of fake *** analyze the abnormal characteristics of fake information from its mechanism of generation and *** view of different exceptional features,we systematically sort out and evaluate the existing studies on false content *** commonly used public datasets,metrics,and performance are categorized and compared,hoping to provide a basis and guidance for related *** study found that the current active social platforms show different *** future direction should point to mining platform features of multi-domain sources,multi-data forms,and multi-language heterogeneity to provide more valuable clues for fake information.

关键词： Fake information Abnormal characteristics Detection methods Public datasets

Automated federated learning‐based adversarial attack and defence in industrial control systems

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IET Cyber-Systems and Robotics 2024年第2期6卷 109-127页

作者： Guo-Qiang Zeng Jun-Min Shao Kang-Di Lu Guang-Gang Geng Jian Weng College of Cyber security Jinan UniversityGuangzhouChina National Joint Engineering Research center of network security Detection and Protection Technology Jinan UniversityGuangzhouChina College of Information Science and Technology Donghua UniversityShanghaiChina

With the development of deep learning and federated learning(FL),federated intrusion detection systems(IDSs)based on deep learning have played a significant role in securing industrial control systems(ICSs).However,adversarial attacks on ICSs may compromise the ability of deep learning-based IDSs to accurately detect cyberattacks,leading to serious ***,in the process of generating adversarial samples,the selection of replacement models lacks an effective method,which may not fully expose the vulnerabilities of the *** authors first propose an automated FL-based method to generate adversarial samples in ICSs,called AFL-GAS,which uses the prin-ciple of transfer attack and fully considers the importance of replacement models during the process of adversarial sample *** the proposed AFL-GAS method,a lightweight neural architecture search method is developed to find the optimised replacement model composed of a combination of four lightweight basic ***,to enhance the adversarial robustness,the authors propose a multi-objective neural archi-tecture search-based IDS method against adversarial attacks in ICSs,called MoNAS-IDSAA,by considering both classification performance on regular samples and adver-sarial robustness *** experimental results on three widely used intrusion detection datasets in ICSs,such as secure water treatment(SWaT),Water Distribution,and Power System Attack,demonstrate that the proposed AFL-GAS method has obvious advantages in evasion rate and lightweight compared with other four ***,the proposed MoNAS-IDSAA method not only has a better classification performance,but also has obvious advantages in model adversarial robustness compared with one manually designed federated adversarial learning-based IDS method.

关键词： adversarial attacks adversarial robustness automated federated learning Industrial control system intrusion

An End-To-End Hyperbolic Deep Graph Convolutional Neural network Framework

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 537-563页

作者： Yuchen Zhou Hongtao Huo Zhiwen Hou Lingbin Bu Yifan Wang Jingyi Mao Xiaojun Lv Fanliang Bu School of Information network security People’s Public Security University of ChinaBeijing100038China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing100081China

Graph Convolutional Neural networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured ***,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial ***,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical *** more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic *** HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic ***,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing *** addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention *** the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical *** results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding ***,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhanceme

关键词： Graph neural networks hyperbolic graph convolutional neural networks deep graph convolutional neural networks message passing framework

Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing

High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and security Liuzhou 545000China Guangxi Education System network security Monitoring center Liuzhou 545000China center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

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 ***,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 *** 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.

关键词： Active defense decision-making Cloud boundary network security Intelligent agent reinforcement learning Offensive and defensive game

Multi-party privacy-preserving decision tree training with a privileged party

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Science China(Information Sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of Computing Power network and Information security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： privacy protection decision trees secure multi-party computation secret sharing privileged party

QBIoT:A Quantum Blockchain Framework for IoT with an Improved Proof-of-Authority Consensus Algorithm and a Public-Key Quantum Signature

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Computers, Materials & Continua 2024年第7期80卷 1727-1751页

作者： Ang Liu Qing Zhang Shengwei Xu Huamin Feng Xiu-bo Chen Wen Liu network and Information Management Division Beijing Electronic Science and Technology InstituteBeijing100070China Department of Cyberspace security Beijing Electronic Science and Technology InstituteBeijing100070China Information security Institute Beijing Electronic Science and Technology InstituteBeijing100070China General Office Beijing Electronic Science and Technology InstituteBeijing100070China Information security center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China

The Internet of Things(IoT)is a network system that connects physical devices through the Internet,allowing them to ***,IoT has become an integral part of our lives,offering convenience and smart ***,the growing number of IoT devices has brought about a corresponding increase in cybersecurity threats,such as device vulnerabilities,data privacy concerns,and network *** blockchain technology with IoT has proven to be a promising approach to enhance IoT ***,the emergence of quantum computing poses a significant challenge to the security of traditional classical cryptography used in blockchain,potentially exposing it to quantum *** support the growth of the IoT industry,mitigate quantum threats,and safeguard IoT data,this study proposes a robust blockchain solution for IoT that incorporates both classical and post-quantum security ***,we present the Quantum-Enhanced Blockchain Architecture for IoT(QBIoT)to ensure secure data sharing and integrity ***,we propose an improved Proof of Authority consensus algorithm called“Proof of Authority with Random Election”(PoARE),implemented within QBIoT for leader selection and new block ***,we develop a publickey quantum signature protocol for transaction verification in the ***,a comprehensive security analysis of QBIoT demonstrates its resilience against cyber threats from both classical and quantum *** summary,this research introduces an innovative quantum-enhanced blockchain solution to address quantum security concernswithin the realmof *** proposedQBIoT framework contributes to the ongoing development of quantum blockchain technology and offers valuable insights for future research on IoT security.

关键词： IoT quantum blockchain public-key quantum signature quantum hash function