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Open Identity Summit 2024

作者： Ludwig, Andy Heinl, Michael P. Giehl, Alexander Fraunhofer AISEC Department Product Protection and Industrial Security Garching bei München Germany Technical University of Munich TUM School of Computation Information and Technology Department of Computer Engineering IT Security Germany

ISBN: (纸本)9783885797449

The digitization of shop floors opens up opportunities for innovative applications and business models due to the vast amount of generated data. However, a lot of this potential is currently not utilized because companies consider the risk of data sharing as too high compared to the corresponding benefit. Focusing on the machine tool sector, the research project MINERVA addresses these concerns by experimentally repurposing privacy-enhancing technologies as confidentiality-protecting technologies and applying them to the use case of condition monitoring to protect intellectual property and other information deemed critical by machine tool operators. Thereby, MINERVA’s goal is to reduce the risk of data sharing and support the establishment of data-driven business models in the machine tool sector in the long term. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Differential privacy

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An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

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computers, Materials & Continua 2023年第5期75卷 2397-2412页

作者： Mohammed Mubarak Mustafa Ahmed Abelmonem Khalifa Korhan Cengiz Nikola Ivkovic Department of Information Technology Sri Krishna College of Engineering&TechnologyCoimbatoreIndia Department of Networking and Security College of Information TechnologyUniversity of FujairahFujairahUAE Switching and Traffic Department National Telecommunication InstituteCairoEgypt Department of Computer Engineering Istinye UniversityZeytinburnuIstanbulTurkey Faculty of Organization and Informatics University of ZagrebVarazdinCroatia

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical *** grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the ***,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions *** this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based *** consecutive segments are *** the covering of adjusted clusters,an ideal number of clusters is determined ***,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor *** heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster ***,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long *** assessment results demonstrate that the IEPA performs better than existing conventions.

关键词： Energy consumption improved energy-proficient algorithm internet of things wireless sensor network

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Reinforcement learning-based unknown reference tracking control of HMASs with nonidentical communication delays

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Science China(Information Sciences) 2023年第7期66卷 46-57页

作者： Yong XU Zheng-Guang WU Wei-Wei CHE Deyuan MENG School of Automation Beijing Institute of Technology Institute of Cyber-Systems and Control Zhejiang University College of Mathematics and Computer Science Zhejiang Normal University Department of Automation Qingdao University School of Automation Science and Electrical Engineering Beihang University(BUAA)

This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies assuming that the precise model of the leader is globally or distributively accessible to all or some of the followers, the leader's precise dynamical model is entirely inaccessible to all the followers in this paper. A data-based learning algorithm is first proposed to reconstruct the leader's unknown system matrix online. A distributed predictor subject to communication delays is further devised to estimate the leader's state, where interaction delays are allowed to be nonidentical. Then, a learning-based local controller, together with a discounted performance function, is projected to reach the optimal output synchronization. Bellman equations and game algebraic Riccati equations are constructed to learn the optimal solution by developing a model-based reinforcement learning(RL) algorithm online without solving regulator equations, which is followed by a model-free off-policy RL algorithm to relax the requirement of all agents' dynamics faced by the model-based RL algorithm. The optimal tracking control of HMASs subject to unknown leader dynamics and communication delays is shown to be solvable under the proposed RL algorithms. Finally, the effectiveness of theoretical analysis is verified by numerical simulations.

关键词： heterogeneous multiagent systems HMAS reinforcement learning RL optimal output synchronization communication delays

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Fog enabling technologies in healthcare: A review

Fog enabling technologies in healthcare: A review

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International Conference on Artificial Intelligence, Blockchain, Computing and security, ICABCS 2023

作者： Yadav, Aditya Pradhan, Onesimus Chandra Khanam, Ruqaiya Amrita Computer Science and Engineering Sharda School of Engineering and Technology Sharda University U.P Greater Noida India Department of EECE Center for AI in Medicine Imaging and Forensic Sharda University U.P Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University U.P Greater Noida India

ISBN: (纸本)9781032669663

This paper provides an overview of fog computing healthcare technologies that are playing a huge role in the current healthcare Industry. In this paper, we suggest the Data mining is the most effective option for disease prediction. The suggested framework intends to support health care based on disease symptoms, and health-related data. Gathering and analyzing activity-specific data that may be utilized to gauge the health benefits of exercise and its intensity as well as provide suggestions. Health Informatics makes extensive use of wireless sensor networks (WSNs). Patients at risk for chronic illnesses are increasingly being monitored via wireless and wearable sensors. This ensures that patients follow their treatment schedules and protects them from unexpected assaults. Along with its abilities, the fog computing framework also brings over from the cloud computing platform a number of security flaws that must be solved for the benefit of the user. Fog computing is crucial because it aids in the system’s elimination of latency problems, which are crucial in the healthcare sector. © 2024 The Author(s).

关键词： Fog computing

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Optimized Predictive Modelling for Chronic Kidney Disease: A Hybrid Ensemble Approach with Bacterial Foraging Optimization and SMOTE 6

Optimized Predictive Modelling for Chronic Kidney Disease: A...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Pavan Kumar, V.S. Yadla, Jyothi Parvatham, Niranjan Kumar Lokesh Kumar, P. Tulluri, Usha Amrutha, K. Department of CSE-AIML MLR Institute of Technology Hyderabad India Department of CSE Sir C R Reddy College of Engineering Eluru India Department of CSE CVR College of Engineering Hyderabad India Department of CSE-Cyber Security CMR Engineering College Hyderabad India Bhimavaram India

ISBN: (纸本)9798331522667

Chronic kidney disease is a disease with obvious seriousness of severity. It can cause more severe complications if it further develops, so early diagnosis is very important for disease control and the improvement of the patient’s prognosis. The main goal of this paper is to achieve a novel high-accuracy predictive model for CKD diagnosis through data preprocessing, feature selection, and ensemble modeling. Feature selection is carried out in the research using the Bacterial Foraging Optimization Algorithm (BFO), which optimizes model efficiency and improves accuracy through the exclusion of several non-informative features while taking into consideration significant predictors such as blood pressure and albumin levels. Preprocessing of the data is done by addressing issues of class imbalance by applying the SMOTE method and outliers by applying the IQR method, guaranteeing the provision of quality data set input. The present study adopts AdaBoost and XGBoost in an ensemble form with an accuracy of 98.21%, precision of 1.0000, recall of 0.9000, and an F1 score of 0.9474. As shown in these metrics, the proposed model is stable and can be used in clinical practice. Consequently, the results of this study show that more advanced feature selection integrated with ensemble techniques improves the model’s accuracy for CKD prediction, making it possible to design a tool for early diagnosis of CKD. Future work will be concerned with testing the observed model on various datasets and more importantly – investigating the possibilities for using real-time clinical data, which will allow considering this approach as effective in practice. © 2025 IEEE.

关键词： Clinical research

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Supervised Feature Learning for Offline Writer Identification Using VLAD and Double Power Normalization

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computers, Materials & Continua 2023年第7期76卷 279-293页

作者： Dawei Liang Meng Wu Yan Hu College of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China JinCheng College Nanjing University of Aeronautics and AstronauticsNanjing211156China Engineering Research Center of Electronic Data Forensics Analysis Nanjing210031Jiangsu ProvinceChina

As an indispensable part of identity authentication,offline writer identification plays a notable role in biology,forensics,and historical document ***,identifying handwriting efficiently,stably,and quickly is still challenging due to the method of extracting and processing handwriting *** this paper,we propose an efficient system to identify writers through handwritten images,which integrates local and global features from similar handwritten *** local features are modeled by effective aggregate processing,and global features are extracted through transfer ***,the proposed system employs a pre-trained Residual Network to mine the relationship between large image sets and specific handwritten images,while the vector of locally aggregated descriptors with double power normalization is employed in aggregating local and global ***,handwritten image segmentation,preprocessing,enhancement,optimization of neural network architecture,and normalization for local and global features are exploited,significantly improving system *** proposed system is evaluated on computer Vision Lab(CVL)datasets and the International Conference on Document Analysis and Recognition(ICDAR)2013 *** results show that it represents good generalizability and achieves state-of-the-art ***,the system performs better when training complete handwriting patches with the normalization *** experimental result indicates that it’s significant to segment handwriting reasonably while dealing with handwriting overlap,which reduces visual burstiness.

关键词： Writer identification power normalization vector of locally aggregated descriptors feature extraction

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Investigation of Red Fox Algorithm for Solving Task Assignment Problem in Heterogeneous Unmanned Aerial Vehicle Swarm 3

Investigation of Red Fox Algorithm for Solving Task Assignme...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Manaswitha, K. Issac, Titus Silas, Salaja Terance, J Sebastian Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350372120

In recent years, there have been significant advancements in Unmanned Aerial Vehicles (UAVs), leading to their integration into everyday life. UAVs are constrained by limited energy, communication, and localization. The various challenges in UAV research include localization, routing, navigation, and task assignment. Recently, UAV swarms have been employed across a wide range of applications. This study focuses on solving task assignment problems within UAV swarms, considering the unique multi-objective resource constraints of UAVs. The work explores the feasibility of adopting the Red Fox Algorithm to address the task assignment problem in UAV swarms. The investigation examines two primary input parameters: Task Energy Demand and distance. The evaluation includes energy utilization and shortest distance as performance metrics. Notably, the proposed approach outperforms the Particle Swarm Optimization Algorithm (PSO), resulting in a 17.47% lower energy utilization while maintaining a 17.83% reduction in the shortest distance travelled. By leveraging the Red Fox Algorithm, the work aims to enhance the efficiency and effectiveness of task allocation in UAV swarms, contributing to their broader adoption across various applications. © 2024 IEEE.

关键词： Energy utilization

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Multi-Scale Fusion and Decomposition Network for Single Image Deraining

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IEEE Transactions on Image Processing 2024年 33卷 191-204页

作者： Wang, Qiong Jiang, Kui Wang, Zheng Ren, Wenqi Zhang, Jianhui Lin, Chia-Wen Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China Hangzhou Dianzi University College of Computer Science and Technology Hangzhou310018 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated remarkable success in low-level vision tasks, such as image super-resolution, deraining, and dehazing. The former excels in acquiring local connections with translation equivariance, while the latter is better at capturing long-range dependencies. However, both CNNs and Transformers suffer from individual limitations, such as limited receptive field and weak diversity representation of CNNs during low efficiency and weak local relation learning of SA. To this end, we propose a multi-scale fusion and decomposition network (MFDNet) for rain perturbation removal, which unifies the merits of these two architectures while maintaining both effectiveness and efficiency. To achieve the decomposition and association of rain and rain-free features, we introduce an asymmetrical scheme designed as a dual-path mutual representation network that enables iterative refinement. Additionally, we incorporate high-efficiency convolutions throughout the network and use resolution rescaling to balance computational complexity with performance. Comprehensive evaluations show that the proposed approach outperforms most of the latest SOTA deraining methods and is versatile and robust in various image restoration tasks, including underwater image enhancement, image dehazing, and low-light image enhancement. The source codes and pretrained models are available at https://***/qwangg/MFDNet. © 1992-2012 IEEE.

关键词： Image reconstruction

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Reseed Skipping of Linear Congruential Generator for Multi-level Image Steganography security of Financial Data

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SN computer Science 2023年第2期4卷 124页

作者： Danso, Juliana Mantebea Missah, Yaw Marfo Gyamfi, Enoch Opanin Dankwa, Stephen Kwabena, Sarpong Department of Computer Science Kwame Nkrumah University of Science and Technology (KNUST) Kumasi Ghana School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Cyber Security and Computer Engineering Technology School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Upper East Region Navrongo Ghana School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

This paper proposed a multi-level image steganography model that integrates with image and plaintext encryption schemes. The model starts by partitioning the cover-image pixel positions into two regions of respective purposes, one is reserved to hold the data, and through the implementation of the dynamic difference expansion principle, the other sloughs some parts to the reserved region in responsive to the size of the data to embed. Continuously reseeding and initializing the parameter values of a Linear Congruential Generator (LCG), generate a set of random pixel positions as members of the reserved region. Before data embedding, the region reserved to hold the data as well as the data bits to embed, are then encrypted using AES with respective keys. Encryption keys are randomly generated using a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG), the Fortuna algorithm. Multiplex security nature of using a randomization generator to reserve image region for encryption and encrypting plaintext data before performing steganography still ensures stability in computational complexities better than a logarithmic O(n log n). A visual inspection by evaluators who made 3000 choices to correctly differentiate stego images from its cover-images shows that only 47 choices were correct, 1884 choices were wrong, while 1069 choices were uncertain. An experiment shows an average Embedding Rates (ERs) of 0.00109 bpp and an average PSNR of 72. This concludes, as our integrated steganography and image encryption which implements the dynamic difference expansion principle, outputs encrypted stego mages that can deceive the naked human eyes into identifying them as rather cover-images, its embedding rate is faster (i.e. ER < 0.1), suitably good, and ensures better imperceptibility of cover-image alterations. When comparing these experimental results with existing methods, our proposed model shows significant competitiveness. We even show real-life scenarios on how this

关键词： AES encryption scheme Dynamic difference expansion principle Linear congruential generator Pseudo-random number generator Reserving room before encryption Steganography

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Development of Modified Homomorphic Encryption for IIoT on Textual Data

Development of Modified Homomorphic Encryption for IIoT on T...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Rathinakumar, P. Dhivya, R. Prasath, S. Jayapriya, K.N. Kumar, M.Sathish Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Ece Vadapalani Campus Tamilnadu Chennai600026 India M.Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India Mohamed Sathak A J College of Engineering Department of Mechanical Engineering Tamil Nadu Chennai603103 India Kathir College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore641062 India Karpagam College of Engineering Department of Cyber Security Tamilnadu Coimbatore641032 India

ISBN: (纸本)9798350383348

Digitalizing existing systems for mass production is what's known as 'Smart Industry' or the Industrial Internet of Things (IIoT). As a result of the interoperability of modern industrial equipment with IoT devices, a dizzying array of manufactured goods is being created. Problems with device availability, product dependability and product mobility for smart implementation, performance, security, scalability, interoperability, and energy efficiency are some of the research hurdles in Industrial IoT. This study presents a novel homomorphic encryption technique with improved performance. An analysis is conducted to assess the appropriateness of Homomorphic encryption for the Industrial Internet of Things (IIoT), and a refined version of the current algorithm is suggested and put into practice. The method's findings are compared to those of current algorithms in terms of performance metrics such as encryption time, decryption time, and throughput. It is determined that the technique is effective for different sizes of plain text blocks. © 2024 IEEE.

关键词： Decryption Encryption Homomorphism Internet of Things (IoT)

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