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security analysis and secured access design for networks of image remote sensing

Digital Communications and networks 2025年第1期11卷 136-144页

作者： Juan Zhao Haibo Dai Xiaolong Xu Hao Yan Zheng Zhang Chunguo Li School of Network Security Jinling Institute of TechnologyNanjing 211169China School of IoT Nanjing University of Posts and Telecommunications(NJUPT)Nanjing 210003China Jiangsu Key Lab.of Big Data Security and Intelligent Processing NJUPTNanJing 210003China School of Information Science and Engineering Southeast UniversityNanjing 210096China

The secured access is studied in this paper for the network of the image remote *** sensor in this network encounters the information security when uploading information of the images wirelessly from the sensor to the central collection *** order to enhance the sensing quality for the remote uploading,the passive reflection surface technique is *** one eavesdropper that exists nearby this sensor is keeping on accessing the same networks,he may receive the same image from this *** goal in this paper is to improve the SNR of legitimate collection unit while cut down the SNR of the eavesdropper as much as possible by adaptively adjust the uploading power from this sensor to enhance the security of the remote sensing *** order to achieve this goal,the secured energy efficiency performance is theoretically analyzed with respect to the number of the passive reflection elements by calculating the instantaneous performance over the channel fading *** on this theoretical result,the secured access is formulated as a mathematical optimization problem by adjusting the sensor uploading power as the unknown variables with the objective of the energy efficiency maximization while satisfying any required maximum data rate of the eavesdropper ***,the analytical expression is theoretically derived for the optimum uploading *** simulations verify the design approach.

关键词： Image remote sensing Secured access Energy efficiency Sensor transmit power Secured access design

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A Weighted Discrete Wavelet Transform-Based Capsule network for Malware Classification 27th

A Weighted Discrete Wavelet Transform-Based Capsule Network ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Qiao, Tonghua Cao, Chunjie Zou, Binghui Tao, Fangjian Cheng, Yinan Zhang, Qi Sun, Jingzhang Lab of Heterogeneous Network Security and Governance Haikou China School of Cyberspace Security Hainan University Haikou China Faculty of Data Science City University of Macau China

ISBN: (纸本)9783031781278

The rise of sophisticated malware poses a grave threat to computer security, challenging traditional detection methods. Traditional malware detection techniques, which primarily rely on feature engineering and defining rules to identify malware, are gradually failing to comprehensively and accurately detect emerging malware variants. To address this problem, we proposed an efficient and accurate novel malware classification model based on capsule network with Weighted Discrete Wavelet Transform (WDWTCN). In the field of malware classification, datasets are predominantly composed of grayscale images, which present samples in a single-channel format. To more adeptly capture the key features within malware samples, WDWTCN incorporates the Weighted Discrete Wavelet Transform (WDWT) as a preprocessing step for input data. By converting the single-channel input of grayscale images into a four-channel input, our model becomes significantly more efficient in extracting feature information from malware samples. We used ResNet50 as the feature extraction layer of the capsule network. By integrating global and local features, the model can comprehensively understand and classify malware, thereby enhancing the detection performance of malware. Finally, to validate the performance of the WDWTCN, we conducted an extensive experimental evaluation on three large datasets, i.e., BIG2015, MalImg and BODMAS. The experimental results showed that WDWTCN achieves significant improvements in malware detection accuracy, reaching 98.93%, 99.21% and 96.14% accuracy, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Discrete wavelet transforms

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AdaptFRCNet: Semi-supervised adaptation of pre-trained model with frequency and region consistency for medical image segmentation

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Medical Image Analysis 2025年 103卷 103626页

作者： He, Along Wu, Yanlin Wang, Zhihong Li, Tao Fu, Huazhu College of Computer Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300350 China 138632 Singapore Haihe Lab of ITAI Tianjin300459 China

Recently, large pre-trained models (LPM) have achieved great success, which provides rich feature representation for downstream tasks. Pre-training and then fine-tuning is an effective way to utilize LPM. However, the application of LPM in the medical domain is hindered by the presence of a large number of parameters and a limited amount of labeled data. In clinical practice, there exists a substantial amount of unlabeled data that remains underutilized. Semi-supervised learning emerges as a promising approach to harnessing these unlabeled data effectively. In this paper, we propose semi-supervised adaptation of pre-trained model with frequency and region consistency (AdaptFRCNet) for medical image segmentation. Specifically, the pre-trained model is frozen and the proposed lightweight attention-based adapters (Att_Adapter) are inserted into the frozen backbone for parameter-efficient fine-tuning (PEFT). We propose two consistency regularization strategies for semi-supervised learning: frequency domain consistency (FDC) and multi-granularity region similarity consistency (MRSC). FDC aids in learning features within the frequency domain, and MRSC aims to achieve multiple region-level feature consistencies, capturing local context information effectively. By leveraging the proposed Att_Adapter along with FDC and MRSC, we can effectively and efficiently harness the powerful feature representation capability of the LPM. We conduct extensive experiments on three medical image segmentation datasets, demonstrating significant performance improvements over other state-of-the-art methods. The code is available at https://***/NKUhealong/AdaptFRCNet. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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Adversarial training via multi-guidance and historical memory enhancement

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Neurocomputing 2025年 619卷

作者： Zhao, Chenyu Qian, Yaguan Wang, Bin Gu, Zhaoquan Ji, Shouling Wang, Wei Zhang, Yanchun School of Science Zhejiang University of Science and Technology China Network and Data Security China China School of Computer Science and Technology Zhejiang University China Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University China Institute for Sustainable Industries and Liveable Cities Victoria University Australia

Deep neural networks (DNNs) are often susceptible to the influence of adversarial examples, potentially leading to severe security issues. Adversarial training stands out as one of the most effective defenses. In this paper, we empirically demonstrate that weakly robust models maintain resilience to adversarial examples generated on a fully trained robust model. Building on this observation, we introduce a novel weight ensemble training method named Multi-Guided Adversarial Training (MGAT). MGAT improves both clean and adversarial accuracy through two guiding strategies: self-guidance and expert-guidance. Self-guidance promotes diversity in decision boundaries among member models, while expert-guidance aligns them more closely with natural decision boundaries. Additionally, MGAT enhances the historical memory of model weights using an exponential moving average (EMA) with a memory factor, thereby better memorizing the weights of weakly robust models. Our experiments demonstrate that MGAT is highly effective in defending against a range of adversarial attacks. Notably, MGAT achieves an accuracy of 55.48% against AutoAttack, marking a 10.92% improvement over standard adversarial training on CIFAR-10. © 2024 Elsevier B.V.

关键词： Generative adversarial networks

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OSE: On-Site State of Health Estimation for Li-ion Battery using Real-Time Field data

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7452-7462页

作者： Chen, Yanwen Zhao, Cong Yang, Shusen Zhao, Peng Ren, Xuebin Han, Qing Yang, Yuqian Wu, Shuaijun Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics Shaanxi Xi'an710049 China Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics The Ministry of Education Key Lab for Intelligent Networks and Network Security Shaanxi Xi'an710049 China

On-site lithium-ion battery state of health (SoH) estimation is of crucial importance for reliable operations of electric vehicles (EVs). Yet, due to the low-quality of unlabeled real-time field data, diverse operating environments of in-service EVs, and limited computational capability of onboard devices, existing techniques established on data from well-controlled experimental environments are not practical for real world EVs' SoH estimation. Accurate and rapid SoH estimation based on field data of in-service EVs still remains quite challenging. To tackle this challenge, we present an on-site SoH estimation (OSE) method using in-service EV field data through a new knowledge embedded deep transfer learning (DTL) model. Initially, an universal data preprocessing approach integrating mechanism knowledge is designed to process low-quality data under diverse operating environments. Then, we develop a domain adaptive hybrid deep neural network (DAHDNN) model suitable for unlabeled field data, which can be deployed via edge cloud collaborative framework to meet actual computational capability. We demonstrate the superiority of our method across four real datasets, where, OSE's estimation error is decreased by up to 78.5% compared with the state-of-the-art methods. The results indicate that the proposed method has good generalizability and reliability for SoH estimation on real-time field data. © 2015 IEEE.

关键词： State of charge

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EVPIR: Efficient and Verifiable Privacy-Preserving Image Retrieval in Cloud-assisted Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Li, Mingyue Li, Yuntao Du, Ruizhong Jia, Chunfu Shao, Wei Hebei University School of Cyber Security and Computer Baoding071000 China Key Lab on High Trusted Information Sys tem in Hebei Province Hebei China College of Cyber Sciences Nankai university Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China Jinan250014 China

With the proliferation of mobile devices and the advancement of cloud computing capabilities, cloud-assisted Internet of Things (IoT) attracts increased attention based on its computational and storage advantages. Upon these conveniences, there also raises privacy concerns that numerous solutions have been proposed to solve it. However, existing methods suffer from challenges such as low retrieval accuracy, inefficiency in large-scale image retrieval, and lack of efficient result verification. In this paper, we propose an Efficient Verifiable Privacy-preserving Image Retrieval scheme (EVPIR). Specifically, we design a hierarchical graph index to significantly enhance retrieval efficiency, which organizes image feature vectors into a multi-level structure, establishing connections between neighboring nodes within each layer and creating a highly structured and efficient retrieval framework. During the retrieval process, we employ greedy search algorithm to navigate these connections and identify the closest neighbors across different levels, which make the proposed multi-level approach reduce the search space at each level, achieving faster and more accurate retrieval. Furthermore, we design an efficient dynamic verifiable framework leveraging Chameleon hash functions and BLS signatures where we utilize Chameleon hash nodes based on Merkle Hash Trees (MHT) to enable dynamic updates of the verification tree and employ BLS signatures to construct multiple verification nodes for effectively shortening the verification path. Finally, security analysis shows that EVPIR can defend various threat models and extensive experiments further demonstrates that EVPIR can improve retrieval and verification efficiency. (Figure presented) © 2014 IEEE.

关键词： Differential privacy

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Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4140-4155页

作者： Nhan Vo, Van Quoc Long, Nguyen Dang, Viet-Hung Dac Ho, Tu Tran, Hung Chatzinotas, Symeon Tran, Dinh-Hieu Sanguanpong, Surasak So-In, Chakchai Duy Tan University Faculty of Information Technology Da Nang550000 Viet Nam Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Department of Information Security and Communication Technology Trondheim7034 Norway National Economics University DATCOM Lab Faculty of Data Science and Artificial Intelligence College of Technology Hanoi12116 Viet Nam University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust Esch-sur-Alzette4365 Luxembourg Kasetsart University Faculty of Engineering Department of Computer Engineering Bangkok10900 Thailand Khon Kaen University Applied Network Technology College of Computing Khon Kaen40002 Thailand

This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. In the second phase, a secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions. © 2020 IEEE.

关键词： Drones

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Correctness verification in database outsourcing: A trust-based fake tuples approach

Correctness verification in database outsourcing: A trust-ba...

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8th International Conference on Information Systems security, ICISS 2012

作者： Ghasemi, Simin Noferesti, Morteza Hadavi, Mohammad Ali Nogoorani, Sadegh Dorri Jalili, Rasool Data and Network Security Lab Sharif University of Technology Tehran Iran

ISBN: (纸本)9783642351297

An important security challenge in database outsourcing scenarios is the correctness verification of query results. The proposed approaches in the literature, impose high overhead on both the service provider and specially the clients. In this paper, we propose the Trust-Based Fake Tuples approach to audit the correctness of query results. In this approach, some fake tuples are included among the real ones in order to verify the correctness of the results. The experience learnt from past results is used in this paper to evaluate the trust toward the service provider. This trust value is used to tune the number of fake tuples and subsequently the imposed overhead. As the trust value toward the service provider increases, the number of fake tuples and imposed overhead decreases. The experimental results confirm the effectiveness of our approach on the reduction of overhead while keeping the probability of incorrect results in its acceptable level. © Springer-Verlag 2012.

关键词： Outsourcing

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A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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Science China(Information Sciences) 2019年第3期62卷 196-198页

作者： Yusong DU Baodian WEI Huang ZHANG School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Chongqing Key Lab of Computer Network and Communication Technology

Dear editor,Discrete Gaussian sampling, that is, sampling from a discrete Gaussian distribution DΛ,σ,cwith parameter σ> 0 and center c∈R~n over an ndimensional lattice Λ, has been considered by the cryptography research community as one of the fundamental building blocks of lattice-based cryptography [1, 2]. The simplest lattice is the one-

关键词： A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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Statistical disclosure: Improved, extended, and resisted

Statistical disclosure: Improved, extended, and resisted

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6th International Conference on Emerging security Information, Systems and Technologies, SECURWARE 2012

作者： Emamdoost, Navid Dousti, Mohammad Sadeq Jalili, Rasool Data and Network Security Lab Department of Computer Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9781612082097

Traffic analysis is a type of attack on secure communications systems, in which the adversary extracts useful patterns and information from the observed traffic. This paper improves and extends an efficient traffic analysis attack, called "statistical disclosure attack. " Moreover, we propose a solution to defend against the improved (and, a fortiori, the original) statistical disclosure attack. Our solution delays the attacker considerably, meaning that he should gather significantly more observations to be able to deduce meaningful information from the traffic.

关键词： Anonymity Mix-net Privacy SDA

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