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Central Aggregator Intrusion Detection System for Denial of Service Attacks

computers, Materials & Continua 2023年第2期74卷 2363-2377页

作者： Sajjad Ahmad Imran Raza MHasan Jamal Sirojiddin Djuraev Soojung Hur Imran Ashraf Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan Department of Software Engineering New Uzbekistan UniversityTashkent100007Uzbekistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Vehicle-to-grid technology is an emerging field that allows unused power from Electric Vehicles(EVs)to be used by the smart grid through the central *** the central aggregator is connected to the smart grid through a wireless network,it is prone to cyber-attacks that can be detected and mitigated using an intrusion detection ***,existing intrusion detection systems cannot be used in the vehicle-to-grid network because of the special requirements and characteristics of the vehicle-to-grid *** this paper,the effect of denial-of-service attacks of malicious electric vehicles on the central aggregator of the vehicle-to-grid network is investigated and an intrusion detection system for the vehicle-to-grid network is *** proposed system,central aggregator–intrusion detection system(CA-IDS),works as a security gateway for EVs to analyze andmonitor incoming traffic for possible DoS *** are registered with a Central Aggregator(CAG)to exchange authenticated messages,and malicious EVs are added to a blacklist for violating a set of predefined policies to limit their interaction with the CAG.A denial of service(DoS)attack is simulated at CAG in a vehicle-to-grid(V2G)network manipulating various network parameters such as transmission overhead,receiving capacity of destination,average packet size,and channel *** proposed system is compared with existing intrusion detection systems using different parameters such as throughput,jitter,and *** analysis shows that the proposed system has a higher throughput,lower jitter,and higher accuracy as compared to the existing schemes.

关键词： Denial of service attack vehicle to grid network network security network throughput

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Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformers 27

Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D V...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Mengke Li, Da Yang, Guoqing Cheung, Yiu-Ming Huang, Hui Shenzhen China VCC College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9781643685489

Pre-trained large-scale models have exhibited remarkable efficacy in computer vision, particularly for 2D image analysis. However, when it comes to 3D point clouds, the constrained accessibility of data, in contrast to the vast repositories of images, poses a challenge for the development of 3D pre-trained models. This paper therefore attempts to directly leverage pre-trained models with 2D prior knowledge to accomplish the tasks for 3D point cloud analysis. Accordingly, we propose the Adaptive PointFormer (APF), which fine-tunes pre-trained 2D models with only a modest number of parameters to directly process point clouds, obviating the need for mapping to images. Specifically, we convert raw point clouds into point embeddings for aligning dimensions with image tokens. Given the inherent disorder in point clouds, in contrast to the structured nature of images, we then sequence the point embeddings to optimize the utilization of 2D attention priors. To calibrate attention across 3D and 2D domains and reduce computational overhead, a trainable PointFormer with a limited number of parameters is subsequently concatenated to a frozen pre-trained image model. Extensive experiments on various benchmarks demonstrate the effectiveness of the proposed APF. The source code and more details are available at https://***/research/2024/PointFormer. © 2024 The Authors.

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The Impact of Blockchain Technology for Evaluation of Electronic Medical Certification

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SN computer science 2025年第5期6卷 1-14页

作者： Azam Zia, M. Anwar, Munazza Mehmood, Faisal Razzaq, Abdul Asif, Muhammad Department of Computer Science University of Agriculture Faisalabad Punjab Faisalabad 38000 Pakistan Department of Civil and Transportation Engineering Shenzhen University Shenzhen 518060 China Computer Science Muhammad Nawaz Shareef University of Agriculture Punjab Multan 59300 Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Punjab Lahore Pakistan

Evaluation of Electronic Medical Certification (EMC) is integral to today's healthcare systems since they are a consolidated database of individual patients' medical histories. Security measures and privacy issues are standard in electronic medical certification. Incompatible systems make sharing data across healthcare providers difficult, which may lead to data breaches and a lack of patient authority over their medical data. Blockchain has become essential for real-world corporate applications where confidentiality of information is paramount. The handling of security in medical records through a decentralized peer-to-peer network might be revolutionized by blockchain technology and improving the protection of the interchange of healthcare records. This study proposes a lightweight flask framework-based blockchain strategy to store and display patient status and transaction log information about their disease diagnosis. In a potential Blockchain system, the diseased patients act as assets, and their records contain any prior illnesses as blocks. The report analyzes a lot of national and international EHR standards. It identifies which of these interoperability needs to be addressed, discusses the interoperability challenges in but also outlines the standards. Current blockchain-based EHR frameworks. Without the suggested has the requirement for centralized systems. By using this framework, the healthcare industry can be provided safer ways to while also providing the exchange of health information. It offers features of immutability, security, and user control over stored records. This work contributes to an understanding of what potential Blockchain technology has offered an interoperable application in the EHR frameworks. Meet the blockchain-based EHR framework. Multiple national and international EHR standards. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Cryptography Decentralized Electronic medical certification (EMC) Transaction

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Scene and Aerial Image Classification Based on Advanced Deep Learning Technique 8th

Scene and Aerial Image Classification Based on Advanced Deep...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Mehmood, Maryam Hussain, Farhan Ijaz, Asad Department of Computer and Software Engineering National University of Sciences and Technology Islamabad44000 Pakistan Department of Software Engineering National University of Modern Languages Islamabad Pakistan Department of Mechanical Engineering Mirpur University of Science and Technology MUST AJK Mirpur Pakistan

ISBN: (纸本)9789819786947

Classifying scenes and aerial imagery is a critical component in applications such as land-use analysis, land cover mapping, and remote sensing technologies. Numerous existing models leverage Convolutional Neural Networks for classification, but they often involve intricate designs with a vast number of parameters, necessitating extensive computational power and prolonged training periods. This paper introduces a refined strategy employing a pre-trained EfficientNet B0 framework. Modification of parameters and layers through careful fine-tuning has resulted in notable enhancements in the model's classification precision. The superiority of EfficientNet B0 over comparative CNN architectures was confirmed by testing with three disparate datasets, particularly within the sphere of remote sensing imagery. The EfficientNet B0's superior performance is credited to its optimal structure that strikes a balance between model complexity and efficiency. Effective fine-tuning coupled with judicious computational resource allocation yields substantial accuracy in categorization. Moreover, its adaptability to assimilate remote sensing data exemplifies its widespread applicability. This study accentuates the significance of refining prevailing deep learning models to cater to specified objectives. Tailoring pre-trained models such as EfficientNet B0 enables marked advancements in classification precision, circumventing the necessity for intricate systems or significant computational demands. This methodology is promising for elevating both the proficiency and impact of scene and aerial image classification across a range of fields, with particular implications for remote sensing endeavors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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Behavior Trees and State Machines in Robotics Applications

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IEEE Transactions on software engineering 2023年第9期49卷 4243-4267页

作者： Ghzouli, Razan Berger, Thorsten Johnsen, Einar Broch Wasowski, Andrzej Dragule, Swaib Chalmers University of Technology Department of Computer Science and Engineering Gothenburg40530 Sweden Ruhr University Bochum Faculty of Computer Science Bochum44801 Germany Chalmers University of Gothenburg Department of Computer Science and Engineering Gothenburg40530 Sweden University of Oslo Department of Informatics Oslo0316 Norway IT University of Copenhagen Software Development Group Copenhagen2300 Denmark

Autonomous robots combine skills to form increasingly complex behaviors, called missions. While skills are often programmed at a relatively low abstraction level, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State machines have been the go-to language to model behavior for decades, but recently, behavior trees have gained attention among roboticists. Originally designed to model autonomous actors in computer games, behavior trees offer an extensible tree-based representation of missions and are claimed to support modular design and code reuse. Although several implementations of behavior trees are in use, little is known about their usage and scope in the real world. How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are concepts in behavior trees and state machines used in actual applications? This paper is a study of the key language concepts in behavior trees as realized in domain-specific languages (DSLs), internal and external DSLs offered as libraries, and their use in open-source robotic applications supported by the Robot Operating System (ROS). We analyze behavior-tree DSLs and compare them to the standard language for behavior models in robotics: state machines. We identify DSLs for both behavior-modeling languages, and we analyze five in-depth. We mine open-source repositories for robotic applications that use the analyzed DSLs and analyze their usage. We identify similarities between behavior trees and state machines in terms of language design and the concepts offered to accommodate the needs of the robotics domain. We observed that the usage of behavior-tree DSLs in open-source projects is increasing rapidly. We observed similar usage patterns at model structure and at code reuse in the behavior-tree and state-machine models within the mined open-source projects. We contribute all extracted models as a dataset, hoping to inspire the commu

关键词： Modeling languages

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DFU_XAI: A Deep Learning-Based Approach to Diabetic Foot Ulcer Detection Using Feature Explainability

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Biomedical Materials and Devices 2024年第2期2卷 1225-1245页

作者： Biswas, Shuvo Mostafiz, Rafid Paul, Bikash Kumar Uddin, Khandaker Mohammad Mohi Hadi, Md. Abdul Khanom, Fahmida Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Institute of Information Technology Noakhali Science and Technology University Department of Software Engineering Daffodil International University Department of Computer Science and Engineering Dhaka International University Department of Computer Science: Information Technology University of Nebraska Omaha Department of Mathematics American International University – Bangladesh

Diabetic foot ulcer (DFU) is a potentially fatal complication of diabetes. Traditional techniques of DFU analysis and therapy are more time-consuming and costly. Artificial intelligence (AI), particularly deep neural networks, has demonstrated remarkable effectiveness in medical applications. Despite this, the lack of explainability of deep learning models is currently viewed as a key hurdle to using these approaches in actual clinical settings. In this research, we present the DFU_XAI framework for assessing the interpretability of explainable-driven deep learning (DL) models. DFU_XAI evaluates five DL models (Xception, DenseNet121, ResNet50, InceptionV3, and MobileNetV2) to establish a transparent DL framework using three state-of-the-art explanation methods: Shapley additive explanation (SHAP), local interpretable model-agnostic explanations (LIME), and gradient-weighted class activation mapping (Grad-CAM). ResNet50 outperformed the other four models with remarkable results: 98.75% accuracy, 99.2% precision, 97.6% recall, 98.4% F1-score, and 98.5% AUC. For the most part, it can locate diabetic foot ulcers precisely on a diabetic foot and discriminate between diabetic foot ulcers and healthy feet in the DFU dataset. A heat map will indicate the precise location of the ulcer that needs care. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2024.

关键词： Convolutional neural networks Deep learning Diabetic foot ulcers Explainable AI

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Density estimation-based method to determine sample size for random sample partition of big data

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Frontiers of computer science 2024年第5期18卷 57-70页

作者： Yulin HE Jiaqi CHEN Jiaxing SHEN Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing and Decision Sciences Lingnan UniversityHong Kong 999077China

Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation *** research and practical applications have confirmed that RSP is an efficient solution for big data processing and ***,a challenge for implementing RSP is determining an appropriate sample size for RSP data *** a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RSP data *** address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RSP data ***,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)***,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RSP data blocks for an increasing sample ***,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of *** results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of *** demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation.

关键词： random sample partition big data sample size Dvoretzky-Kiefer-Wolfowitz inequality kerneldensity estimator probability density function

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OpenCV and its Applications in Artificial Intelligent Systems 5

OpenCV and its Applications in Artificial Intelligent System...

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5th International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Odeh, Ayman Odeh, Nada College Of Engineering Al Ain University Department of Software Engineering Al Ain United Arab Emirates College Of Information Technology UAEU Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350354690

This paper explores the utilization of OpenCV (Open-Source computer Vision Library) in artificial intelligence (AI) systems, elucidating its pivotal role in advancing various applications across diverse domains. OpenCV, renowned for its comprehensive functionalities and real-time processing capabilities, has become instrumental in tasks such as object detection (OD), facial recognition (FR), and image processing (IP). However, despite its widespread adoption, there remains a need for a thorough examination of its integration within AI frameworks and the optimization of its functionalities. Through a comprehensive analysis, this study delineates the advantages, limitations, and potential areas for improvement of OpenCV in AI applications. Case studies exemplifying its efficacy in OD, FR, and autonomous systems further elucidate its practical implications. By delving into these aspects, this paper not only underscores the significance of OpenCV in AI but also provides valuable insights for researchers, developers, and practitioners aiming to leverage its capabilities in their endeavors. This study collectively underscores the significant role of OpenCV in enhancing the capabilities of AI systems. © 2024 IEEE.

关键词： Adversarial machine learning

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TeluguScriptify: A Custom Deep Learning Model for Handwritten Telugu Text Recognition and Tool Development

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SN computer science 2025年第2期6卷 1-13页

作者： Thara, S. Gaddam, Abhiram Ramakurthi, Chandra Siddartha Basava, Vara Prasad Thupakala, Siddartha Dhanya, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Georgia State University Atlanta GA United States Accenture Pune India Master’s in Business Analytics and Project Management University of Connecticut Hartford CT United States

This paper proposes a new method for transforming handwritten Telugu text into editable electronic documents. Handwritten documents in the Telugu language are of very high historical value. There is, however, very high difficulty in recognizing the various types of style of handwriting since there are no very successful Telugu recognitions to compare with efficiency in comparison with Chinese and Arabic. To solve this problem, the recognition for Telugu script was designed with a deep learning custom model, along with a novel cosine similarity-based pre-processing technique to further enhance the model’s performance. The model architecture contains 3 convolutional blocks, 3 max-pooling layers, 4 dropout layers, 2 skip layers, and 2 dense layers. The experimental results show that the custom deep learning model is the frontrunner, achieving 97.3% testing accuracy with a corresponding validation loss of 0.116, the model beats well-known models such as Dense-Net and Alex-Net. The models were trained and validated on 52 Telugu characters from ‘ (Figure presented.) ’ (aa) to ‘ (Figure presented.) ’ (bandira). The state-of-the-art object detection model, YOLO v8n (8.2 nano) model, is used to detect the text regions in handwritten documents. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alex-Net Convolutional blocks Cosine similarity Dense-Net Telugu

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IsoTGAN: Spatial and Geometrical Constraints at GAN and Transformer for 3D Contour Generation 17th

IsoTGAN: Spatial and Geometrical Constraints at GAN and Tr...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Phuong, Thao Nguyen Duy, Vinh Nguyen Sakaino, Hidetomo FPT Consulting Japan FPT Software Minato Japan Department of Computer Science and Communications Engineering Waseda University Shinjuku Japan

ISBN: (纸本)9789819626434

Image generation in 2D and 3D has become an active research topic in Deep Learning. Single or multiple input images with non-orthogonal views are used for another shape and texture with different viewing angles. On the other hand, computer-Aided Design (CAD) relies on handling-based 3D generation, i.e., isometric view images, from three orthographic view line drawings in 2D. However, since unique viewing pairs of such 2D and 3D images are required to train, SOTA models are insufficient to generate desirable images. More spatial and geometrical constraints remain undone due to less corresponding image features between images. This paper proposes IsoTGAN with a GAN-based generator with the Transformer in its generator, where three images of an object’s front, side, and top view are input. The encoder is trained by the spatial and geometrical relation among three view images. A novel Gaussian Enhanced Euclidean attention mechanism and a geometry-constrained loss function are also proposed for further local image feature enhancement. Extensive experiments on the SPARE3D dataset demonstrate that the proposed IsoTGAN outperforms State-of-the-art (SOTA) models, i.e., DINO, regarding local and global image feature accuracy. This helps generate 3D isometric view images in auto-CAD system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D generation attention CAD GAN Isometric view Transformer

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