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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Qin, Tao Liu, Juanjuan Xue, Wenchao National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100020 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Engineering Agent Construction Management Office of Navy Logistics Department Beijing100036 China

ISBN: (纸本)9783030786083

The rapid development of deep learning has brought a new development direction for image inpainting, changing the traditional image inpaiting algorithm, which can only repair the problem of small area damage based on the structure and texture of the damaged image. In recent years, image inpainting algorithm based on deep learning has received widespread attention from industry and academia so that it has made great progress. However, the current image inpainting algorithm based on deep learning still has the problem of consuming so much time. In order to solve the above problem, an end-to-end image inpainting algorithm suitable for real-time scene was proposed. The mask of the damaged image generated by D-linkNet network, the edge information of the damaged image, and damaged image were used to control the network input, which avoided the damage to the existing semantics of the image and retained the intact image outside the damaged. On this basis, in order to improve the performance of image inpainting, Convolutional Block Attention Module (CBAM) was used in the residual network. Experimental results show that, compared with edge information-based deep learning algorithm Edge Connect, the repair speed is twice as fast as Edge Connect while the repair results are similar. © 2021, Springer Nature Switzerland AG.

关键词： Learning algorithms

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A unitary weights based one-iteration quantum perceptron algorithm for non-ideal training sets

arXiv

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arXiv 2023年

作者： Liu, Wenjie Gao, PeiPei Wang, Yuxiang Yu, Wenbin Zhang, Maojun Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

In order to solve the problem of non-ideal training sets (i.e., the less-complete or over-complete sets) and implement one-iteration learning, a novel efficient quantum perceptron algorithm based on unitary weights is proposed, where the singular value decomposition of the total weight matrix from the training set is calculated to make the weight matrix to be unitary. The example validation of quantum gates {H, S, T, CNOT, Toffoli, Fredkin} shows that our algorithm can accurately implement arbitrary quantum gates within one iteration. The performance comparison between our algorithm and other quantum perceptron algorithms demonstrates the advantages of our algorithm in terms of applicability, accuracy, and availability. For further validating the applicability of our algorithm, a quantum composite gate which consists of several basic quantum gates is also illustrated. © 2023, CC BY.

关键词： Singular value decomposition

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An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

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computers, Materials & Continua 2020年第10期65卷 743-760页

作者： Peipeng Yu Zhihua Xia Jianwei Fei Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareJiangsu Engineering Center of Network MonitoringJiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologyNanjing University of Information Science&TechnologyNanjing210044China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Coronaviruses are a well-known family of viruses that can infect humans or ***,the new coronavirus(COVID-19)has spread *** countries in the world are working hard to control the coronavirus ***,many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system,which leads to the mass spread of *** a powerful tool,artificial intelligence(AI)has been successfully applied to solve various complex problems ranging from big data analysis to computer *** the process of epidemic control,many algorithms are proposed to solve problems in various fields of medical treatment,which is able to reduce the workload of the medical *** to excellent learning ability,AI has played an important role in drug development,epidemic forecast,and clinical *** research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.

关键词： Artificial intelligence COVID-19 medical applications clinical diagnosis

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MateGraph: Toward Mobile Malware Detection Through Traffic Behavior Graph 24

MateGraph: Toward Mobile Malware Detection Through Traffic B...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Ge, Ruihai Zhang, Yongzheng Si, Chengxiang Zhou, Guoqiao Zhou, Wenchang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China China Assets Cybersecurity Technology CO. Ltd. Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

ISBN: (纸本)9798350319934

As the number of interconnected mobile devices increases exponentially, mobile malware poses a severe threat to cyberspace security. Existing techniques utilize network traffic analysis effectively to identify and detect mobile malware. The majority of these solutions concentrate on the statistical features of malicious traffic or the information of key fields in the packet, but fail to take advantage of rich communication patterns throughout the entire network. In this paper, we present MateGraph, a traffic behavior graph-based approach to detect and classify mobile malware. The key insight of our research is that the communication patterns of a mobile application can be modeled as several traffic behavior graphs in Non-Euclidean space, and thus we can differentiate between malicious and benign applications by using graph convolution network models to learn graph topologies and representations. In MateGraph, we first construct a traffic behavior graph from a given chunk of network traffic within a time window, where a node of the graph is defined by a tuple of destination IP and port while an edge is established by the static and dynamic correlation between the nodes. Next, we present an enhanced graph convolution network for discovering the diverse pattern representations of benign and malicious traffic behavior graphs and achieving high detection performance and efficiency. We have conducted experiments on a publicly available dataset to evaluate MateGraph against several state-of-the-art methods, and the experimental results show that MateGraph has a better performance, the F1 score of which reached 96.57% and the accuracy increased by more than 7%. © 2022 IEEE.

关键词： Convolution

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PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

arXiv

引用

arXiv 2024年

作者： Zhou, Man Su, Shuao Wang, Qian Li, Qi Zhou, Yuting Ma, Xiaojing Li, Zhengxiong School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China Institute for Network Sciences and Cyberspace Tsinghua University China Department of Computer Science and Engineering University of Colorado Denver United States Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification System (AFIS), called PrintListener, which leverages users’ fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users’ fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in real-world scenarios show that Printlistener can significantly improve the attack potency of MasterPrint. © 2024, CC0.

关键词： Friction

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Quantum Algorithms and Experiment Implementations Based on IBM Q

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computers, Materials & Continua 2020年第11期65卷 1671-1689页

作者： Wenjie Liu Junxiu Chen Yinsong Xu Jiahao Tang Lian Tong Xiaoyu Song Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Engineering Jiangsu Maritime InstituteNanjing211100China Department of Electrical and Computer Engineering Portland State UniversityOregon97207USA

With the rapid development of quantum theory and technology in recent years,especially the emergence of some quantum cloud computing platforms,more and more researchers are not satisfied with the theoretical derivation and simulation verification of quantum computation(especially quantum algorithms),experimental verification on real quantum devices has become a new *** this paper,three representative quantum algorithms,namely Deutsch-Jozsa,Grover,and Shor algorithms,are briefly depicted,and then their implementation circuits are presented,*** program these circuits on python with QISKit to connect the remote real quantum devices(i.e.,ibmqx4,ibmqx5)on IBM Q to verify these *** experimental results not only show the feasibility of these algorithms,but also serve to evaluate the functionality of these devices.

关键词： Quantum algorithms implementation circuit IBM Q QISKit program

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Energy Consumption Minimization in Secure Multi-antenna UAV-assisted MEC networks with Channel Uncertainty

arXiv

引用

arXiv 2023年

作者： Mao, Weihao Xiong, Ke Lu, Yang Fan, Pingyi Ding, Zhiguo The Engineering Research Center of Network Management Technology for High Speed Railway Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Beijing National Research Center for Information Science and Technology The Department of Electronic Engineering Tsinghua University Beijing100084 China The School of Electrical and Electronic Engineering The University of Manchester ManchesterM13 9PL United Kingdom

This paper investigates the robust and secure task transmission and computation scheme in multi-antenna unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where the UAV is dual-function, i.e., aerial MEC and aerial relay. The channel uncertainty is considered during information offloading and downloading. An energy consumption minimization problem is formulated under some constraints including users’ quality of service and information security requirements and the UAV’s trajectory’s causality, by jointly optimizing the CPU frequency, the offloading time, the beamforming vectors, the artificial noise and the trajectory of the UAV, as well as the CPU frequency, the offloading time and the transmission power of each user. To solve the non-convex problem, a reformulated problem is first derived by a series of convex reformation methods, i.e., semi-definite relaxation, S-Procedure and first-order approximation, and then, solved by a proposed successive convex approximation (SCA)-based algorithm. The convergence performance and computational complexity of the proposed algorithm are analyzed. Numerical results demonstrate that the proposed scheme outperform existing benchmark schemes. Besides, the proposed SCA-based algorithm is superior to traditional alternative optimization-based algorithm. © 2023, CC BY.

关键词： Energy utilization

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Robust and Secure Federated Learning with Verifiable Differential Privacy

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Chushan Weng, Jian Weng, Jiasi Zhong, Yijian Liu, Jia-Nan Deng, Cunle College of Cyber Security of Jinan University Guangzhou510632 China College of Cyber Security of Jinan University National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Guangdong-Hong Kong-Macao Joint Laboratory of Data Security and Privacy Preserving Guangzhou510632 China Dongguan University of Technology School of Computer Science and Technology Dongguan523808 China

Federated Learning (FL) has emerged as a popular paradigm for training machine learning models on data from multiple sources without the need for data to leave their respective locations. Although FL is widely used, recent research indicates its vulnerability to data privacy breaches and Byzantine attacks. Addressing both threats simultaneously remains challenging, as previous defense mechanisms struggle to balance Byzantine resilience, data privacy, and training accuracy. Although combining Differential Privacy (DP) with Byzantineresilience in FL offers potential solutions, it presents challenges due to theoretical incompatibilities that can impact the performance of large-scale models. Addressing this underexplored challenge, we propose a novel approach that enhances privacy and Byzantine-resilient in FL while maintaining training accuracy. Our method integrates DP with Byzantine-resilience techniques, overcoming the limitations of existing methods that often sacrifice accuracy or even Byzantine resistance. Our experiments carefully analyze the impact of DP noise on training accuracy and Byzantine-resilience, guiding appropriate parameter selection. The experimental results demonstrate that our approach achieves superior global model accuracy compared to previous competitive methods, even with strong privacy constraints. © 2004-2012 IEEE.

关键词： Federated learning

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Transformer-Based Multi-Scale Fusion for Robust Predicting Microsatellite Instability from Pathological Images

Transformer-Based Multi-Scale Fusion for Robust Predicting M...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Taiyuan Hu Haijing Luan Rui Yan Jifang Hu Kaixing Yang Xinyin Han Weier Liu Jiayin He Xiaohong Duan Ruilin Li Fa Zhang Beifang Niu Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Biomedical Engineering University of Science and Technology of China Hefei China Beijing Institute of Genomics Chinese Academy of Sciences Beijing China Scientific Research Center ChosenMed Technology (Zhejiang) Co. Ltd. Hangzhou China School of Medical Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Microsatellite instability (MSI) is a crucial biomarker for guiding the efficacy of immunotherapy and adjuvant chemotherapy, making its detection essential for effective cancer treatment and prognosis. Traditional MSI prediction methods encounter challenges including high costs and limited accuracy under low tumor purity conditions. Recent advancements have explored deep learning for MSI prediction from pathological images, yet these approaches often overlook the multi-scale nature of pathological images and specific pathological features critical for MSI diagnosis. In this study, we proposed MSIscope, a novel Transformer-based method for detecting MSI from pathological images by fusing multi-scale pathological image information. Our approach consists of three key components: 1) ROI selection: we design a region of interest (ROI) selector based on convolutional neural networks and attention mechanisms, selecting tumor regions and important non-tumor regions as our focus; 2) Multi-scale vision expansion and feature extraction: we develop an algorithm that captures a broader view centered on a specified area to obtain a multi-scale field of view. The CTransPath feature extractor is then used to extract features from the image; 3) Multi-scale fusion Transformer: we propose a multi-scale feature aggregator (MS-Transformer) to aggregate contextual features across regions and scales. Our method was experimentally validated on public datasets, achieving an AU-ROC of 0.911 on the TCGA pan-cancer dataset and 0.887 on the TCGA-CRC dataset, surpassing existing methods. Additionally, it maintains high AUROC on datasets with lower tumor purity, outperforming current approaches. These results highlight the potential of MSIscope as an robust method for MSI prediction.

关键词： Pathology Accuracy Biological system modeling Small satellites Predictive models Feature extraction Transformers Robustness Prognostics and health management Tumors

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Self-feedback LSTM regression model for real-time particle source apportionment

引用

Journal of Environmental Sciences 2022年第4期34卷 10-20页

作者： Wei Wang Weiman Xu Shuai Deng Yimeng Chai Ruoyu Ma Guoliang Shi Bo Xu Mei Li Yue Li Trusted AI System Laboratory College of Computer ScienceNankai UniversityTianjin 300350China KLMDASR Tianjin Key Laboratory of Network and Data Security TechnologyTianjin 300350China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and EngineeringNankai UniversityTianjin 300071China Institute of Mass Spectrometry and Atmospheric Environment Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution Jinan UniversityGuangzhou 510632China Guangdong-Hongkong-Macao Joint Laboratory of Collaborative Innovation for Environmental Quality Guangzhou 510632China

Atmospheric particulate matter pollution has attracted much wider attention *** recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments *** demands are summarized,in this paper,as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances,such as the existence of secondary source and similar *** this study,we firstly analyze the possible and potential restraints in single particle source apportionment,then propose a novel three-step self-feedback long short-term memory(SF-LSTM)network for approximating the source *** proposed deep learning neural network includes three modules,as generation,scoring and refining,and regeneration *** from the scoring modules,SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment,meanwhile,the regeneration module calculates the source contribution in a non-linear *** results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators(residual sum of squares,stability,sparsity,negativity)for the ***,in short time-resolution analyzing,SF-LSTM provides better results under the restraint of stability.

关键词： Time series Regression Self-feedback LSTM network Particle source apportionment

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