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MIA-UNet:Multi-Scale Iterative Aggregation U-network for Retinal Vessel Segmentation

Computer Modeling in Engineering & sciences 2021年第11期129卷 805-828页

作者： Linfang Yu Zhen Qin Yi Ding Zhiguang Qin Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China

As an important part of the new generation of information technology,the Internet of Things(IoT)has been widely concerned and regarded as an enabling technology of the next generation of health care *** fundus photography equipment is connected to the cloud platform through the IoT,so as to realize the realtime uploading of fundus images and the rapid issuance of diagnostic suggestions by artificial *** the same time,important security and privacy issues have *** data uploaded to the cloud platform involves more personal attributes,health status and medical application data of *** leaked,abused or improperly disclosed,personal information security will be ***,it is important to address the security and privacy issues of massive medical and healthcare equipment connecting to the infrastructure of IoT healthcare and health *** meet this challenge,we propose MIA-UNet,a multi-scale iterative aggregation U-network,which aims to achieve accurate and efficient retinal vessel segmentation for ophthalmic auxiliary diagnosis while ensuring that the network has low computational complexity to adapt to mobile *** this way,users do not need to upload the data to the cloud platform,and can analyze and process the fundus images on their own mobile terminals,thus eliminating the leakage of personal ***,the interconnection between encoder and decoder,as well as the internal connection between decoder subnetworks in classic U-Net are redefined and ***,we propose a hybrid loss function to smooth the gradient and deal with the imbalance between foreground and *** with the UNet,the segmentation performance of the proposed network is significantly improved on the premise that the number of parameters is only increased by 2%.When applied to three publicly available datasets:DRIVE,STARE and CHASE DB1,the proposed network achieves the accuracy/F1-score of 96.33%/84.34%

关键词： Retinal vessel segmentation security and privacy redesigned skip connection feature maps aggregation hybrid loss function

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Random Coding Responses for Resisting Side-channel Attacks in Client-side Deduplicated Cloud Storage

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IEEE Transactions on Services Computing 2025年第03期18卷 1697-1710页

作者： Ha, Guanxiong Chen, Yuchen Cai, Zhipeng Jia, Chunfu Shan, Xuan Nankai University College of Cryptology and Cyber Science Tianjin China Data Security Technology Tianjin Key Laboratory of Network and Data Security Technology China Tianjin300350 China

Side-channel attacks are widespread in client-side deduplication systems, compromising the privacy of outsourced data. The adversary may infer the existence status of data via the deterministic relations between duplication requests and responses to launch side-channel attacks. Random Response (RARE) is one of the state-of-the-art approaches to overcome this issue, where the cloud server returns the randomized deduplication response for two requests at once to mitigate the risk of side-channel attacks. However, it still has some inherent limitations on communication efficiency and security. In this paper, we propose Random Coding Responses (RACORE), a lightweight and secure multi-chunk coding algorithm to address the limitations of RARE. RACORE achieves efficient multi-chunk coding and obfuscation based on the linear mapping induced by a specially constructed pseudo-random matrix. Compared to existing schemes, RACORE can strike a flexible balance between security and performance by adjusting parameters. Further, we present an enhanced composite matrix generation strategy to extend the coding matrix. Based on this strategy, we design an enhanced coding algorithm RACORE+to improve efficiency. Besides, we put forward a novel redundant chunk selection method to enhance the security of RACORE and RACORE^+. Rigorous security analysis and extensive experimental evaluation demonstrate that both RACORE and RACORE+ can effectively resist side-channel attacks while reducing overhead compared to existing schemes. © 2008-2012 IEEE.

关键词： Sensitive data

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Gray-scale transformation for traffic classification: leveraging local and global features in flow data 3

Gray-scale transformation for traffic classification: levera...

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3rd International Conference on Electronic Information Engineering, Big data, and Computer technology, EIBDCT 2024

作者： Xia, Yuchao Xue, Mutian Li, Wei Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China University of Electronic Science and Technology of China Chengdu China Sichuan University of Media and Communications Chengdu China

ISBN: (纸本)9781510680449

The rapid expansion and diversification of internet traffic pose a pressing demand for accurate classification of diverse application streams in network management and security monitoring. This paper introduces a deep learning-based method for precise classification of diverse internet application streams in network. By transforming raw traffic data into grayscale images and utilizing Convolutional Neural networks (CNNs) and the Swin-Transformer framework, critical information is extracted at both local and global feature levels. The core of this method is offering a visual representation of the structure and content of traffic data. This visual representation enables subsequent feature extraction and classification. Through experimentation across various applications including email, Skype, YouTube, Vimeo, FTP, and Facebook, the proposed method in traffic classification is demonstrated to be effective. This approach boosts traffic classification accuracy, offers innovation in network management and security monitoring, and holds vital importance in optimizing network performance and security. Additionally, it sets the stage for future research in networking data processing and security methodologies. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： network security

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Leaky Autofill: An Empirical Study on the Privacy Threat of Password Managers’ Autofill Functionality 40

Leaky Autofill: An Empirical Study on the Privacy Threat of ...

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40th Annual Computer Security Applications Conference, ACSAC 2024

作者： Fu, Yanduo Wang, Ding College of Cyber Science Nankai University Tianjin300350 China Ministry of Education Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300350 China

ISBN: (纸本)9798331520885

Password managers (PMs) provide users with convenient and robust functionalities to manage their credentials, highly recommended by security experts and major standard bodies. One of the most popular features is the autofill functionality, with which users need a single click or a few clicks to fill in every field in web forms, facilitating the process of completing web forms. However, such indiscriminate autofill brings severe privacy threats. PMs may inadvertently fill data into wrong fields in web forms, even hidden fields, potentially leading to privacy leaks and credential theft. In this paper, we conduct an empirical study evaluating the effectiveness of 30 popular PMs in identifying and handling hidden fields. We focus on the privacy threats posed by the autofill functionality, which fills data into hidden fields. We develop a semi-automated autofill testing tool and explore whether PMs autofill sensitive data into hidden fields across 15 concealment techniques and three web forms, including personal information, credit card, and login forms. Experimental results reveal that every PM autofills data into hidden fields in at least one web form, with an overall filled probability of 58.7% in 1032 scenarios. Further analysis reveals that login forms are the most vulnerable, with a 65.7% probability of hidden fields autofill. Hidden fields concealed by clip-path and content-visibility are filled with passwords by all PMs. Besides, built-in-browser PMs exhibit a 4.07 times higher likelihood of filling data into hidden fields than separately-installed PMs. Even more concerning, built-in-browser PMs, except Safari, autofill passwords into hidden fields under any concealment technique. 37.7% of autofill scenarios with insufficient user interaction pose heightened privacy threats, as users are unaware of autofill content. These privacy threats have been confirmed by popular PMs like LastPass. To mitigate the threats brought by the autofill functionality, we present two

关键词： Visual languages

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NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning 25

NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on ...

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34th ACM Web Conference, WWW 2025

作者： Li, Ken Shi, Bin Wei, Jiazhe Dong, Bo School of Computer Science and Technology Xi’an Jiaotong University Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China School of Distance Education Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9798400712746

Federated Graph Learning (FedGL) is an emerging Federated Learning (FL) framework that learns the graph data from various clients to train better Graph Neural networks(GNNs) model. Owing to concerns regarding the security of such framework, numerous studies have attempted to execute backdoor attacks on FedGL, with a particular focus on distributed backdoor attacks. However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What’s more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack. To address the above significant shortcomings, we propose a non-intrusive graph distributed backdoor attack(NI-GDBA) that does not require backdoor triggers to be injected in the training data. Our attack trains an adaptive perturbation trigger generator model for each malicious client to learn the natural backdoor from the GNN model downloading from the server with the malicious client’s local data. In contrast to traditional distributed backdoor attacks on FedGL via trigger injection in training data, our attack on different datasets such as Molecules and Bioinformatics have higher attack success rate, stronger persistence and stealth, and has no negative impact on the performance of the global GNN model. We also explore the robustness of NI-GDBA under different defense strategies, and based on our extensive experimental studies, we show that our attack method is robust to current federated learning defense methods, thus it is necessary to consider non-intrusive distributed backdoor attacks on FedGL as a novel threat that requires custom d

关键词： Federated learning

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10B-doped MCP detector developed for neutron resonance imaging at Back-n white neutron source

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Nuclear science and Techniques 2024年第8期35卷 58-68页

作者： Qiang Li Li‑Jiao Wang Jing‑Yu Tang Xiang‑Biao Qiu Zhen Chen Mao‑Yuan Zhao Chang‑Jun Ning Kai Pan Wei Xu Tao Li Su‑Peng Lu Han Yi Rui‑Rui Fan Chang‑Qing Feng Rong Zhang Xiao‑Yang Sun Qi An Hao‑Fan Bai Jiang‑Bo Bai Jie Bao Ping Cao Qi‑Ping Chen Yong‑Hao Chen Zeng‑Qi Cui An‑Chuan Fan Fan‑Zhen Feng Min‑Hao Gu Chang‑Cai Han Zi‑Jie Han Guo‑Zhu He Yong‑Cheng He Yang Hong Yi‑Wei Hu Han‑Xiong Huang Wei Jiang Zhi‑Jie Jiang Zheng‑Yao Jin Ling Kang Bo Li Gong Li Xiao Li Yang Li Jie Liu Rong Liu Shu‑Bin Liu Yi‑Na Liu Guang‑Yuan Luan Jie Ren Zhi‑Zhou Ren Xi‑Chao Ruan Zhao‑Hui Song Kang Sun Zhi‑Xin Tan Sheng‑Da Tang Jin‑Cheng Wang Peng‑Cheng Wang Zhao‑Hui Wang Zhong‑Wei Wen Xiao‑Guang Wu Xuan Wu Cong Xia Yong‑Ji Yu Guo‑Hui Zhang Hang‑Chang Zhang Lin‑Hao Zhang Qi‑Wei Zhang Xian‑Peng Zhang Yu‑Liang Zhang Yue Zhang Zhi‑Yong Zhang Zhi‑Hao Zhou Ke‑Jun Zhu Chong Zou Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Dongguan Songshan Lake Future School Dongguan 523808China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230027China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China North Night Vision Science and Technology(Nanjing)Research Institute Co.Ltd Nanjing 211106China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of Modern Physics Fudan UniversityShanghai 200433China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China China Academy of Engineering Physics Institute of Nuclear Physics and ChemistryMianyang 621900China USTC archaeometry lab University of Science and Technology of ChinaHefei 230026China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China China Shipbuilding Trading Co. LtdBeijing 100044China

Neutron resonance imaging(NRI)has recently emerged as an appealing technique for neutron *** complexity surpasses that of conventional transmission imaging,as it requires a high demand for both a neutron source and ***,the progression of NRI technology has been sluggish since its inception in the 1980s,particularly considering the limited studies analyzing the neutron energy range above *** white neutron source(Back-n)at the China Spallation Neutron Source(CSNS)provides favorable beam conditions for the development of the NRI technique over a wide neutron energy range from eV to ***-sensitive microchannel plates(MCP)have emerged as a cutting-edge tool in the field of neutron detection owing to their high temporal and spatial resolutions,high detection efficiency,and low *** this study,we report the development of a 10B-doped MCP detector,along with its associated electronics,data processing system,and NRI experiments at the *** heavy elements such as gold,silver,tungsten,and indium can be easily identified in the transmission images by their characteristic resonance peaks in the 1–100 eV energy range;the more difficult medium-weight elements such as iron,copper,and aluminum with resonance peaks in the 1–100 keV energy range can also be *** particular,results in the neutron energy range of dozens of keV(Aluminum)are reported here for the first time.

关键词： Neutron resonance imaging 10B doped MCP detector White neutron source Sample nuclide identification

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A novel network intrusion detection model based on two-phase detection and manually labeling

A novel network intrusion detection model based on two-phase...

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2021 International Conference on Internet of Things and Machine Learning, IoTML 2021

作者： Zhang, Yu Zhou, Yangbo Ma, Xiaowei Tianjin Key Laboratory of Network and Data Security Technology College of Cyber Science Nankai University Tianjin China

ISBN: (数字)9781510653269

ISBN: (纸本)9781510653252

network intrusion detection system (NIDS) is a tool that can detect various network attacks by analyzing network traffic. In recent years, traditional machine learning and deep learning methods have been widely used in NIDS. And these works achieved high detection rate and low false positive rate when identifying common attacks (such as DoS). However, the existing models performed poor in detecting rare or unseen network attacks. In this paper, we design a novel network intrusion detection model based on two-phase detection and manually labeling. This two-phase detection model (TPDM) is a multi-classification system, and it can classify network traffic into benign or specific attack type. TPDM gains the overall accuracy 98.35%, and average F1-score 99.23% on UNSW-NB15 dataset. The experiment shows that TPDM performs better than state of the art in detecting rare attacks with few training samples. Besides, it can detect unknown network attacks and then label these attacks. © SPIE.

关键词： Intrusion detection

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Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy

arXiv

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arXiv 2024年

作者： Zhang, Hengran Bi, Keping Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China

Utility and topical relevance are critical measures in information retrieval (IR), reflecting system and user perspectives, respectively. While topical relevance has long been emphasized, utility is a higher standard of relevance and is more useful for facilitating downstream tasks, e.g., in Retrieval-Augmented Generation (RAG). When we incorporate utility judgments into RAG, we realize that the topical relevance, utility, and answering in RAG are closely related to the three types of relevance that Schutz discussed from a philosophical perspective. They are topical relevance, interpretational relevance, and motivational relevance, respectively. Inspired by the dynamic iterations of the three types of relevance, we propose an Iterative utiliTy judgmEnt fraMework (ITEM) to promote each step of the cycle of RAG. We conducted extensive experiments on multigrade passage retrieval and factoid questionanswering datasets (i.e., TREC DL, WebAP, and NQ). Experimental results demonstrate significant improvements in utility judgments, ranking of topical relevance, and answer generation upon representative baselines, including multiple single-shot utility judging approaches. Our code and benchmark can be found at https://anonymous.4open. science/r/ITEM-B486/. Copyright © 2024, The Authors. All rights reserved.

关键词： Search engines

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Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework

arXiv

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arXiv 2024年

作者： Chen, Lu Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China

Retrieval-augmented generation (RAG) has emerged as a popular solution to mitigate the hallucination issues of large language models. However, existing studies on RAG seldom address the issue of predictive uncertainty, i.e., how likely it is that a RAG model’s prediction is incorrect, resulting in uncontrollable risks in real-world applications. In this work, we emphasize the importance of risk control, ensuring that RAG models proactively refuse to answer questions with low confidence. Our research identifies two critical latent factors affecting RAG’s confidence in its predictions: the quality of the retrieved results and the manner in which these results are utilized. To guide RAG models in assessing their own confidence based on these two latent factors, we develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers. We also introduce a benchmarking procedure to collect answers with the option to abstain, facilitating a series of experiments. For evaluation, we introduce several risk-related metrics and the experimental results demonstrate the effectiveness of our approach. Our code and benchmark dataset are available at https://***/ictbigdatalab/RC-RAG. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Temporal Refinement Graph Convolutional network for Skeleton-Based Action Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1586-1598页

作者： Zhuang, Tianming Qin, Zhen Ding, Yi Deng, Fuhu Chen, Leduo Qin, Zhiguang Raymond Choo, Kim-Kwang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu Sichuan610054 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan610054 China School of Advanced Technology Suzhou Jiangsu215123 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human skeleton data, which has served in the aspect of human activity recognition, ought to be the most representative biometric characteristics due to its intuitivity and visuality. The state-of-the-art approaches mainly focus on improving modeling spatial correlations within graph topologies. However, the interframes motional representations are also of vital importance, and we argue that they are worth paying attention to and exploring. Therefore, a temporal refinement module with contrastive learning mechanism is proposed, fuzing as a complementary to the conventional spatial graph convolution layer. In addition, in order to further exploiting the interframe variances and generalizing graph convolutional network (GCN) operation to temporal dimension, a temporal-correlation matrix is introduced to effectively capture dynamic dependencies within frame-pairs, enhancing semantic feature representation. Moreover, since GCN is a typical local operator which lacks of capability to fully model the long-term relations along spatial and temporal variation, to move beyond the limitation, a spatial-temporal cascaded aggregation (STCA) module is designed to enlarge the receptive filter scale. The overall recognition framework consists of three above novelties, which is capable of achieving remarkable performance by evaluating on benchmark datasets (i.e., NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, and Kinetics Skeleton 400). Extensive experiments demonstrate the effectiveness of the proposed framework, e.g., performing recognition accuracy rate of 90.9% and 96.8% on NTU RGB+D 60, 87.9% and 88.9% on NTU RGB+D 120. © 2020 IEEE.

关键词： Convolution

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