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Blockchain Applications in 5G networks: Incentives 21

Blockchain Applications in 5G Networks: Incentives

引用

21st IEEE international symposium on Intelligent systems and Informatics, SISY 2023

作者： Qose, Silvana Zoltan, Rajnai Doctoral School in Safety and Security Sciences Obuda University Budapest Hungary

ISBN: (纸本)9798350343366

In a cross, multi-system world, blockchain technology is rapidly used to facilitate transaction security and safety, supply - Chain, cryptographic protocols, and identity authentication. This facilitation occurs because blockchain technology, a digital ledger of exchanges cloned and distributed across an entire computer network, structures the recording of critical data unchanging and transparent to anyone and everyone, making it difficult to change or hack without being discovered. It is not surprising that given these advantages, Blockchain is gaining traction in various industries, including financial services, energy, healthcare, real estate, telecoms, and others involving several entities whose accounts or activities could not be verified. The advantages of Blockchain may also be applied to another critical sector for different states: national security. Through its capacity to assure confidence in communication and information technology (ICT) and malware protection and verification, crucial to 5G and communication infrastructure, the technology shows potential for resolving some of today's cybersecurity challenges. © 2023 IEEE.

关键词： Blockchain

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Chaotic Water Wave Optimization and Support Vector Machine of Role-Based Access Control Policies in Enterprise IAM

Chaotic Water Wave Optimization and Support Vector Machine o...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Priyanka, K. Harshini, Chikka Aluvala, Srinivas Abbas, Haider Mohammed Ram Kumar, Ramaswamy Padmanathan Nitte Meenakshi Institute of Technology Department of Ise Bengaluru India Department of Cse Hyderabad India Sr University Department of Computer Science and Engineering Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Bachupally Hyderabad India

ISBN: (纸本)9798350375442

The access control state of a system is complex, and Machine Learning (ML) based access control decision engines have demonstrated advantages of policies. However, struggle to effectively implement Role-Based Access Control (RBAC) policies because require large enterprises Identity and Access Management (IAM) system with a high volume of users and access logs. This paper, proposed Chaotic Water Wave Optimization (CWWO) algorithm combined with a Support Vector Machine (SVM) to classify users based on selected features, leading to more accurate and secure role to assignment. The dataset used is obtained from amazon and pre-processing is done using Min-max normalization, which scales the feature to a specific range, ensuring that different features are on same scale. Feature selection is performed using the CWWO algorithm, which helps in selecting relevant feature and reduce dimensionality. The classification using SVM for binary classification, which assigns relevant feature and handle large amounts of data, achieving better accuracy. The proposed ensemble model technique for ship motion detection is achieving better outcomes such as high accuracy of 0.98, precision of 0.97, F1-score of 0.96 and recall of 0.98 and respectively. The existing technique such Random Forest (RF) and Decision Tree (DT) are evaluated of proposed method. © 2024 IEEE.

关键词： Random forests

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WGAN-Based Synthetic Data Generation for Modeling Cyberattacks in Power Transmission systems 8

WGAN-Based Synthetic Data Generation for Modeling Cyberattac...

引用

8th international symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Gondhi, Spoorthy Reddy Janak, Urmisha Reddy Raja, Ashok Asrari, Arash Purdue University Northwest Electrical and Computer Engineering Department HammondIN United States Purdue University Northwest Computer Information Technology and Graphics Department HammondIN United States

ISBN: (纸本)9798331540104

The integration of diverse infrastructures in modern-day power systems facilitates unauthorized access and data manipulation by adversaries, as these systems heavily rely on Information and Communication Technology (ICT) for monitoring and control. A significant challenge within these power networks is the risk of operational disruptions, such as congestion and voltage instability, resulting from stealthy false data injection (FDI) cyberattacks. Different from the existing work, this paper proposes a solution by introducing a defense framework that utilizes a Wasserstein Generative Adversarial Network (WGAN) to generate synthetic data. This synthetic data closely resembles the output from actual Phasor Measurement Units (PMU) and is developed by training the WGAN with extensive real PMU datasets. Additionally, mixing synthetic and real data when sending it to the Supervisory Control and Data Acquisition (SCADA) system adds layers of complexity and obscures the data landscape for attackers, thereby hindering their ability to detect vulnerabilities and anomalies. © 2024 IEEE.

关键词： Phasor measurement units

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Algebraic Computations in Anonymous VANET 1

引用

12th international Conference on Networked systems (NETYS)

作者： Kowalski, Dariusz R. Mosteiro, Miguel A. Powlette, Austin Augusta Univ Augusta GA USA Pace Univ New York NY 10038 USA

ISBN: (数字)9783031673214

ISBN: (纸本)9783031673207;9783031673214

In the area of development of AI/ML applications in Vehicular Adhoc networks (VANET), a highly dynamic environment, efficient algebraic distributed computations are of utmost importance. On the other hand, there is a growing concern about privacy of users/drivers. One of the solutions is to perform computations assuming anonymity of the users. There is already a large amount of work on this topic in the general Anonymous Dynamic Network model, however the obtained theoretical guarantees are not suitable for very large-scale networks, such as VANET. In this work, we propose an anonymous algebraic computation framework tailored for VANET, called Anonymous Vehicular Adhoc networks (A-VANET). We introduce heuristic changes to the Restricted Methodical Counting (RMC) protocol aiming to speed up performance in A-VANET with respect to the theoretical bounds in general Anonymous Dynamic networks. We evaluate this protocol on traces of taxi trips in New York City extracted from publicly available data from 2013, and on a highway traffic environment modeled by a set of path graphs. Both inputs are highly dynamic including also recurrent disconnections. Our results show that, for the parameter combinations tested and for networks with good expansion, RMC is sub-quadratic and even linear under some conditions. Therefore, even the theoretical upper bound proved as a function of connectivity parameters is loose by a factor of more than n7. These results show the promise of further exploring the question of what is the optimal running time for algebraic computations in A-VANET and other practically-motivated Anonymous Dynamic networks with limited messages, memory and disconnections.

关键词： Vehicular Ad-hoc networks Algebraic Computations Anonymous Dynamic networks

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Proceedings - 2023 IEEE 24th international symposium on a World of Wireless, Mobile and Multimedia networks, WoWMoM 2023

Proceedings - 2023 IEEE 24th International Symposium on a Wo...

引用

24th IEEE international symposium on a World of Wireless, Mobile and Multimedia networks, WoWMoM 2023

ISBN: (纸本)9798350331653

The proceedings contain 72 papers. The topics discussed include: online domain adaptive classification for mobile-to-edge computing;information flow graph for distributed caching without newcomers over a broadcast medium;ARLCL: anchor-free ranging-likelihood-based cooperative localization;a picture is worth 1,000 millimeters: combining vision and Wi-Fi to improve localization;equalizing access to latency-critical services based on in-network computing;handling demand heterogeneity in UAV-aided content caching in communication-challenged environments;gesture recognition with mmWave Wi-Fi access points: lessons learned;disaggregated mobile core for edge city services;and admission control with latency considerations for 5G mobile edge computing.

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Multi-level Optimal Control with Neural Surrogate Models 26

Multi-level Optimal Control with Neural Surrogate Models

引用

26th international symposium on Mathematical Theory of networks and systems (MTNS)

作者： Kalise, Dante Loayza-Romero, Estefania Morris, Kirsten A. Zhong, Zhengang Imperial Coll London Dept Math London England Univ Waterloo Dept Appl Math Waterloo ON Canada Imperial Coll London Ctr Proc Syst Engn London England

Optimal actuator and control design is studied as a multi-level optimisation problem, where the actuator design is evaluated based on the performance of the associated optimal closed loop. The evaluation of the optimal closed loop for a given actuator realisation is a computationally demanding task, for which the use of a neural network surrogate is proposed. The use of neural network surrogates to replace the lower level of the optimisation hierarchy enables the use of fast gradient-based and gradient-free consensus-based optimisation methods to determine the optimal actuator design. The effectiveness of the proposed surrogate models and optimisation methods is assessed in a test related to optimal actuator location for heat control. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Optimal actuator design Optimal control of distributed parameter systems Supervised learning and neural networks Consensus-based optimisation

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TOPOLOGICAL CLASSIFICATION OF SMALL AND MEDIUM SIZED URBAN ROAD networks - A CASE STUDY OF INDIAN CITIES

TOPOLOGICAL CLASSIFICATION OF SMALL AND MEDIUM SIZED URBAN R...

引用

IEEE international Geoscience and Remote Sensing symposium (IGARSS)

作者： Thottolil, Rahisha Kumar, Uttam Int Inst Informat Technol Bangalore IIITB Spatial Comp Lab Ctr Data Sci Bangalore 560100 Karnataka India

ISBN: (纸本)9798350320107

Recently, India overtook China to become the world's most populous country. A larger part of the Indian population living in small to medium and large towns and cities have been facing serious problems of traffic congestion and vehicular transportation in urban areas. Consequently, urban road and street networks have occupied a core position in the functioning of cities. Therefore, assessment of the topological patterns of road infrastructure is crucial for improving the performance of transportation since the spatial pattern of the current road networks have greatly impacted the urban mobility. This paper attempts to analyze the topological patterns of road networks of small and medium-sized Indian cities in terms of connectivity and coverage measures using graph theory. Cities have been prioritized with respect to population ranging from 20,000 to 0.5 million. As a result, 505 such cities were selected in this study. The road maps of individual cities were split into lines at intersections, which were then converted to network with attribute parameters such as number of edges and vertices, and were subsequently used to compute topological (connectivity and coverage) measures such as Alpha, Beta, Gamma, Grid Tree Pattern (GTP), Eta indices, Network Density (ND), Edge Graph Density (EGD) and Nodal Graph Density (NGD). The study revealed that on an average, only 41% of the small and medium-sized Indian cities have greater connectivity (for example, Alpha > 0.8) and only 5% cities have network density higher than 5 km. This indicates that the road and street networks are not distributed evenly throughout the Indian cities. Furthermore, smaller cities that are located close to/near large metropolitan cities have higher connectivity and coverage, whereas smaller cities placed far away from metropolitan areas have less development in the road transportation systems.

关键词： Road network topology connectivity coverage Open Street Map

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, V. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 IEEE.

关键词： computerized tomography

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Heart Disease Prediction and Its Important Parameters Analysis Using Machine Learning

Heart Disease Prediction and Its Important Parameters Analys...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Pant, Mohit Joshi, Namita Vidyarthi, Anurag Mohan, Lalit Graphic Era Hill University Department of Ece Uttarakhand Bhimtal India College of Veterinary Sciences and Animal Husbandry Anduat Department of Veterinary Public Health & Epidemiology Ayodhya India Graphic Era Deemed to Be University Department of Ece Uttrakhand Dehradun India Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350375442

In recent time the number of people dying and suffering from heart disease is very high also these cases are increasing day by day. In heart disease the diagnosis at early phase is very crucial. Machine learning can help in the prediction of heart diseases very accurately just by providing patient data as input. To have good accuracy of prediction the only requirement is that the learning dataset must be as big as possible. The purpose of this research is to compare the accuracies of classification models used to predict heart disease by using the largest dataset available for heart disease prediction, check out the heatmap to find the correlation between different parameters involved in the dataset and to find the most important parameter with importance factor for predicting the heart disease. Random Forest model is having the highest 92.44% accuracy followed by SVM and decision tree in predicting heart disease. Logistic regression had the least accuracy among all the models used. ST slope and oldpeak has very high correlation factor of 0.52 and exercise angina and max heart rate have very low correlation of -0.38. ST slope is the most important factor for predicting heart disease among all other factors given in the dataset and fasting blood sugar is the least important parameter used in prediction of heart disease. © 2024 IEEE.

关键词： Prediction models

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Target Detection Algorithm for Drone Aerial Images based on Deep Learning

Target Detection Algorithm for Drone Aerial Images based on ...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Liu, Tao Zhang, Bohan Heilongjiang Institute of Technology College of Computer Science and Technology Harbin China China University of Geosciences School of Information Engineering Wuhan China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan China

ISBN: (纸本)9798350375442

In recent years, the rapid development of drones has brought tremendous changes to many fields. From refined management in agriculture and forestry to aerial surveying in urban planning, the popularity of UAVs provides efficient and accurate data support for various industries, promoting social intelligence and technological progress. Meanwhile, deep learning methods represented by computer vision provide powerful tools for research such as image processing and object recognition. The research results of HE-YOLOX-ASFF (Highly Efficient You Only Look Once eXtend with Adaptive Spatial Feature Fusion) algorithm for target detection in UAV aerial images in this article can help improve the recognition accuracy and efficiency of drone remote sensing images, and apply it to fields such as smart agriculture, urban management, and environmental monitoring. At present, detection technologies for large-scale targets have achieved good detection results in clear backgrounds. However, drone aerial images often have complex backgrounds, small target volumes, limited available features, and high target density, making them prone to occlusion. Due to the above reasons, existing target detection algorithms have low detection accuracy for weak and small targets in aerial images under complex backgrounds, and are prone to missed and false detections. Therefore, the detection method of weak aerial targets in complex backgrounds remains a challenging issue. The experimental results show that in identifying pedestrians and bicycles, the parameter values are relatively high, at 33.3 and 65.1, respectively, while the FPS (frames per second) is relatively low, at 63 and 38, respectively. This indicates that the algorithm requires more parameters to process these targets and may affect processing speed. © 2024 IEEE.

关键词： Drones

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