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Adaptive Attribute-Based Honey Encryption: A Novel Solution for Cloud Data Security

computers, Materials & Continua 2025年第2期82卷 2637-2664页

作者： Reshma Siyal Muhammad Asim Long Jun Mohammed Elaffendi Sundas Iftikhar Rana Alnashwan Samia Allaoua Chelloug School of Computer Science and Engineering Central South UniversityChangsha410083China EIAS LAB College of Computer and Information Sciencesand Center of Excellence in Quantum and Intelligent ComputingPrince Sultan UniversityRiyadh11586Saudi Arabia Gianforte School of Computing Montana State UniversityBozemanMT 59717USA Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.

关键词： Cybersecurity data security cloud storage hadoop encryption and decryption privacy protection attribute-based honey encryption

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Generative Image Steganography Based on Latent Space Vector Coding and Diffusion Model 1

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2nd International Conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Liu, Weisong Yang, Jianfeng Huang, Jiewen El-Latif, Ahmed A. Abd Zhou, Zhili School of Artificial Intelligence Guangzhou University Guangzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

Image steganography is a technology that embed secret information within a cover image to obtain a stego-image for covert communication. The transmission of undetectable stego-images via social media can facilitate secure and efficient covert communication. Recently, the generative image steganography has achieved promising performance with the rapid development of generative models. However, existing generative image steganography suffers from issues, i.e., the low quality of the generated stego-image and the limited hiding capacity. To address these issues, this paper proposes a generative image steganography scheme based on latent space vector coding and diffusion model. It consists of an optimized diffusion denoising network, the latent space vector coding mapping network, and the information extraction network. In this scheme, the secret information is encoded as a latent space vector, which is then transformed in the input diffusion model to generate the stego-image. Experimental results demonstrate that this scheme achieves superior performance compared with existing generative image steganography methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Vectors

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Artificial sensory neurons and their applications

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Journal of Semiconductors 2025年第1期46卷 108-128页

作者： Jiale Shao Hongwei Ying Peihong Cheng Lingxiang Hu Xianhua Wei Zongxiao Li Huanming Lu Zhizhen Ye Fei Zhuge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingbo 315201China School of Electronic and Information Engineering Ningbo University of TechnologyNingbo 315211China State Key Laboratory of Environment-friendly Energy Materials Southwest University of Science and TechnologyMianyang 621010China Institute of Wenzhou Zhejiang UniversityWenzhou 325006China State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200072China

With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly *** limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a *** computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI *** neurons and synapses are the two core components of neuromorphic computing *** perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of *** work reviews recent advances in artificial sensory neurons and their ***,biological sensory neurons are briefly ***,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working ***,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and ***,challenges faced by artificial sensory neurons at both device and system levels are summarized.

关键词： artificial sensory neurons artificial perception systems neuromorphic computing artificial intelligence

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Deep Learning Classification of En Face Multi-spectral Optical Coherence Tomography Images 29

Deep Learning Classification of En Face Multi-spectral Optic...

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Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIX 2025

作者： Assiotis, Nicholas Photiou, Christos Thrapp, Andrew Tearney, Guillermo J. Pitris, Costas University of Groningen Faculty of Science and Engineering Groningen Netherlands KIOS Center of Excellence Dept. of Electrical & Computer Engineering University of Cyprus Nicosia Cyprus Harvard Medical School Massachusetts General Hospital Wellman Center for Photomedicine BostonMA United States

ISBN: (纸本)9781510683587

Colorectal cancer (CRC) is one of the top causes of malignancy. Although screening has significantly reduced CRC mortality, colonoscopy suffers from inadequate sampling of the tissue but, also, high cost, since many polyps that are resected and processed are benign. To alleviate this burden on the healthcare system, a "leave-in-situ" strategy is being considered. To employ such a strategy, highly accurate, in vivo, evaluation of the polyps is required, a need which can be addressed by Optical Coherence Tomography (OCT). However, thus far, OCT studies focusing on morphological features have not been proven effective. To improve the classification, the spectral information of the OCT interferogram can be exploited. In this study, we propose the use of multi-spectral analysis of en face OCT images, i.e., the utilization of images created from different bands of the spectrum, to classify human colon polyps as benign or malignant potential. Multiple, narrow-band, images at different center wavelengths and their ratios were created from 143 samples and ranked according to their predictive potential. The top three spectral features were combined to create a color multi-spectral image. The multi-spectral images were subsequently classified using a DenseNet deep learning model. The resulting accuracy, sensitivity and specificity were 91%, 94%, and 88% respectively. The proposed approach must be expanded to include more polyps and explore more sophisticated multi-spectral methods. However, these preliminary results provide evidence that this method has the potential to improve the accuracy of OCT and, in the future, enable "leave-in-situ" approaches. © 2025 SPIE.

关键词： Optical coherence tomography Image classification Polyps Tumor growth modeling Deep learning Performance modeling Error control coding Colon Tissues Tunable filters

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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Fault-tolerant Mobile Service Offloading in Mobile Edge Computing

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IEEE Transactions on Automation science and engineering 2025年

作者： Long, Tingyan Xia, Yunni Zhou, Mengchu Li, Jianqi Ma, Yong Ai-Turki, Yusuf Guizhou University School of Computer Science and Technology Guiyang550025 China Chongqing University College of Computer Science Chongqing400030 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Global energy interconnection research institute Co. Ltd Beijing102209 China Jiangxi Normal University School of Computer and Information Engineering Nanchang China King Abdulaziz University K. A. Care Energy Research and Innovation Center The Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering Jeddah21589 Saudi Arabia

Mobile edge computing (MEC) is an evolving paradigm for rendering services through network-accessible resources deployed over Internet of Things (IoT) nodes at the edge. Nevertheless, an MEC environment usually employs thousands of physical machines connected via hundreds of switches/routers that communicate and coordinate to deliver computing service. In such complicated systems, faults caused by software, human errors, and hardware are often unavoidable. The edge of network presents a dynamic environment with great quantities of terminals, high mobility of mobile devices, heterogeneous applications, and intermittent traffic. In such an environment, MEC can suffer from unbalanced resource provisioning and interruptions of faults occurring at different levels, which further causes task faults and affects service quality. To address this challenge, this work proposes a novel fault-tolerant offloading method for handling faults by leveraging a reinforcement-learning-based service offloading decision model. The model synthesizes a Dueling Deep Q Network (DQN)-based algorithm for deciding user offloading behaviors and an adaptive checkpointing method for improving task execution reliability. For the purpose of model validation and comparison, extensive simulations are conducted. Numerical results clearly demonstrate that the proposed method is highly effective and outperforms existing methods. © 2004-2012 IEEE.

关键词： Mobile applications

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Turning rough surfaces into non-line-of-sight cameras

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Optica 2025年第5期12卷 626-634页

作者： Li, Wenwen Zhou, Yijun Li, Wei Chen, Yutao Huang, Xin Dai, Chen Zeng, Jianwei Goyal, Vivek K. Xu, Feihu Pan, Jian-Wei Hefei National Research Center for Physical Sciences the Microscale School of Physical Sciences University of Science and Technology of China Hefei230026 China Shanghai Research Center for Quantum Science CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Electrical and Computer Engineering Boston University 8 St. Mary’s Street BostonMA02215 United States

Overcoming the spatial mixing of reflection and scattering from rough surfaces to see a hidden scene is a difficult challenge with a wide range of applications. Existing methods counter the mixing effect using active time-of-flight or coherence-based measurements but at the cost of expensive and dedicated optical systems. Here, we derive a model to accurately describe the microscale scattering properties of a rough surface and show it is well-conditioned for inversion. This enables computational passive non-line-of-sight (NLOS) imaging with unprecedented capabilities. With an ordinary monochrome camera, we demonstrate real-time and high-resolution passive NLOS imaging with spatial resolution of sub-mm, temporal resolution of 25 fps, field of view of near 90◦, and full-color recovery. Furthermore, our approach permits high-quality keyhole NLOS imaging through a mm-scale keyhole and, notably, allows for non-invasive calibration and imaging without any manipulation of the hidden scene. These results open a new avenue for the development ofNLOS imaging, scattering imaging, and relevant applications to real-world conditions. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Image resolution

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Time-dependent global sensitivity analysis of the Doyle-Fuller-Newman model

arXiv

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arXiv 2025年

作者： Zonta, Elia Buha, Ivana Jovanovic Spinola, Michele Weißinger, Christoph Bungartz, Hans-Joachim Jossen, Andreas Technical University of Munich School of Engineering and Design Department of Energy and Process Engineering Arcisstraße 21 Munich80333 Germany Technical University of Munich School of Computation Information and Technology Department of Computer Science Boltzmannstraße 3 Garching85748 Germany Capgemini Engineering Center of Excellence Battery Frankfurter Ring 81 Munich80807 Germany

The Doyle-Fuller-Newman model is arguably the most ubiquitous electrochemical model in lithium-ion battery research. Since it is a highly nonlinear model, its input-output relations are still poorly understood. Researchers therefore often employ sensitivity analyses to elucidate relative parametric importance for certain use cases. However, some methods are ill-suited for the complexity of the model and appropriate methods often face the downside of only being applicable to scalar quantities of interest. We implement a novel framework for global sensitivity analysis of time-dependent model outputs and apply it to a drive cycle simulation. We conduct a full and a subgroup sensitivity analysis to resolve lowly sensitive parameters and explore the model error when unimportant parameters are set to arbitrary values. Our findings suggest that the method identifies insensitive parameters whose variations cause only small deviations in the voltage response of the model. By providing the methodology, we hope research questions related to parametric sensitivity for time-dependent quantities of interest, such as voltage responses, can be addressed more easily and adequately in simulative battery research and beyond. © 2025, CC BY.

关键词： Sensitivity analysis

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UAV-NIDD: A Dynamic Dataset for Cybersecurity and Intrusion Detection in UAV Networks

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IEEE Transactions on Network science and engineering 2025年

作者： Hadi, Hassan Jalil Cao, Yue Khan, Muhammad Khurram Ahmad, Naveed Hu, Yulin Fu, Chao Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430073 China King Saud University Center of Excellence in Information Assurance Riyadh Saudi Arabia Prince Sultan University College of Computer and Information Science Riyadh Saudi Arabia Wuhan University School of Electronic Information China Information and Communication Technology Co. LtdChina China

Unmanned Aerial Vehicles (UAVs) are necessary for numerous tasks but are vulnerable to cyber threats due to their widespread use and connectivity. The lack of a comprehensive dataset necessitates the development of effective detection and mitigation solutions. Our work introduces UAV-NIDD, a new dataset that addresses the gaps in understanding and countering both cyber and physical threats in UAV networks. It includes three distinct attack scenarios: compromised UAV initiating a network-wide attack, access point compromised network-wide intrusion, and compromised Ground Control Station (GCS) establishing a network-wide attack. We develop a real-time testbed for creating UAV-NIDD (Unmanned Aerial Vehicles-Network Intrusion Detection Dataset), incorporating UAV devices, data collection tools, and controllers. Our testbed facilitates cyberattack execution and data gathering under normal and attack conditions. Our dataset covers various cyber-attacks like Scanning, Reconnaissance, DoS, DDoS, GPS Jamming & Spoofing, MITM, Replay, Evil Twin, Brute-Force, and Fake Landing packet attacks. Additionally, UAV-NIDD presents a valuable resource for AI and ML solutions, strengthening UAV networks against evolving cyber threats. Moreover, we offer open access and cooperative innovation in terms of long-term updating of dataset. © 2013 IEEE.

关键词： Network intrusion

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Ore genesis and critical metal enrichment using deep learning algorithms in Gunga Pb-Zn deposit, Southern Pakistan: Constraints from sphalerite geochemistry and isotopic compositions

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Journal of Geochemical Exploration 2025年 275卷

作者： Gul, Muhammad A. Zhang, Huishan Yang, Yu Ren, Guangli Sun, Chao Zhao, Xiaojian Yousuf, Tahir Bin Shah, Ahmed Awan, Rizwan Sarwar Faisal, Mohamed Yang, Xiaoyong CAS Key Laboratory of Crust-Mantle Materials and Environments University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Comparative Planetology University of Science and Technology of China Hefei230026 China MNR Key Laboratory for the Study of Focused Magmatism and Giant Ore Deposits Xi'an Center of Geological Survey CGS Xi'an710054 China Department of Computer Science and IT Virtual University Lahore Pakistan Center of Excellence in Mineralogy University of Baluchistan Quetta Pakistan School of Environment and Resources Engineering Hefei University of Technology Hefei China Department of Geology Faculty of Science Suez Canal University El-Ismailia41522 Egypt School of Geosciences and Info-Physics Central South University Changsha410083 China

The Kirthar Fold and Thrust Belt in southern Pakistan is widely recognized as a major metallogenic province owing to the extensive presence of Pb-Zn deposits within the Jurassic sedimentary shelf sequence. One of these deposits, the Gunga Pb-Zn deposit, is a significant economic ore deposit situated within the Anjira Formation and comprises two mineralization zones, the Upper Mineralization Zone (UMZ) and Lower Mineralization Zone (LMZ). Despite the scarcity of documentation on the metallogenic source, origin, mineralization temperature, and other physicochemical conditions, our research is the first to explore the metallogenic differentiation and ore genesis of the deposit by examining minor and trace elements in sphalerite and isotopic analyses (S-Pb). The Gunga Pb-Zn deposit, amounting to 6.86 Mt. with 2.1 % and 11.4 % Pb and Zn, respectively, is linked to the interbedded limestone and shale unit of the Anjira Formation, and approximately below the depth of 200–250 m of the Jurassic-Cretaceous contact, the mineralization is believed to occur in the upper section of the Formation. Sphalerite is the dominant ore mineral, galena is a subordinate mineral, and pyrite is present in the main ore minerals. Sphalerite in the LMZ was found in a vein style, whereas it was disseminated in the UMZ and enriched in (medians) Ge (166 ppm), Cd (541 ppm), Pb (139 ppm), Ag (42 ppm), and Hg (68 ppm), lacking In (0.17 ppm), Co (6 ppm), and Mn (19). Based on trace element concentrations, the LMZ has a reduced environment, higher sulfur fugacity, and higher mineralization temperature (180–200 °C) than the UMZ, which has a lower mineralization temperature (130–180 °C). The presence of black shales, low temperatures, and high sulfur fugacity are the key parameters for Ge enrichment in the Gunga deposit. The δ34S values range from −10 to +5.6 ‰, suggesting that reduced sulfur was produced by both the BSR and TSR mechanisms, with basinal brines and seawater being the main sources of sulfur

关键词： Sulfur

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