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Cloud Data Anomaly Detection using Machine Learning 4

Cloud Data Anomaly Detection using Machine Learning

引用

4th international conference on Ubiquitous computing and Intelligent Information systems, ICUIS 2024

作者： Karthik Kumar, G.V. Achary, Rathnakar Shelke, Chetan J. Alliance School of Advanced Computing Alliance University Bangalore India

ISBN: (纸本)9798331529635

In cybersecurity, anomaly detection has one of the most important roles in discovering threats that are a menace to an organization's information security. This paper describes the implementation of an anomaly detection system employing Isolation Forests of the Azure Function kind. Utilizing the possibility of the cloud service offered by Azure, the solution provides an effective anomaly detection. Raw data is normalized, features are reconstructed to address cases of missing data and the model then applied is the Isolation Forest with the purpose of differentiating normal and anomalous behavior. Standard performance measures, like accuracy, are used to measure the performance of the model. Furthermore, we give a clearer understanding of the model through the confusion matrix and the correlation heat map, respectively. That is why our approach shows good results in anomaly detection with a low percentage of false positives, which makes it appropriate for use in cloud systems. The integration with Azure Functions further confirms the simplicity, efficiency, and accessibility of serverless computing in more complex cybersecurity solutions. © 2024 ieee.

关键词： Cybersecurity

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Improving Fog computing Infrastructure with Qos-Aware Resource Provisioning 4

Improving Fog Computing Infrastructure with Qos-Aware Resour...

引用

4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dhabliya, Dharmesh Kuppuraj, T. Ojha, Varun Primmia, D.R. Sharma, Divya Mathur, Sanjiv Vishwakarma Institute of Technology Department of Information Technology Pune India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Pg Management Karnataka Bangalore India

ISBN: (纸本)9798331518981

In today's internet-enabled world, it's possible for everything to link to anything else. Because of this, a new way of working known as the Global Internet of Things has emerged, and it has begun to produce massive amounts of data. While it may be difficult to glean insights from streaming data, doing so has the potential to unleash technological marvels that will benefit society at large. It is critical to have a system in place to store, process, and forecast the data. Another active field of study, infrastructure development has seen a shift from centralised server systems to distributed cloud systems and is continuing to do so. There is a lot of room for investigation into the cloud's shortcomings in processing streaming data and making decisions in near real-time. Now that we have a network, the next step is to find a way to make it more useful and to distribute the load more evenly across the nodes via efficient routing. So that the network doesn't fail suddenly from too much traffic, the allocation is based on the present node load and the total network load. A balanced load on each node in the network is another result of optimised route selection. We built and tested a simple RL algorithm for an insignificant network that leverages the Q-Table. To get around the Q-Table's high level of complexity, an RL technique based on neural networks was suggested, which performs better on big networks. Over all time periods, the Proposed Deep RL technique outperforms Open Shortest Path First (OSPF) and Deep Neural Networks (DNN), achieving an accuracy of almost 92% in minimising average latency. In real-time applications, when minimising latency is crucial, its improved efficiency is evident. © 2024 ieee.

关键词： Fog computing

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A Review and Threat Analysis of Machine Learning in Relation to Fog and Edge computing 5

A Review and Threat Analysis of Machine Learning in Relation...

引用

5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Bharadwaj, Chinmoy Ul Islam, Atowar Bharadwaj, Sippee Sarma, Priyanka University of Science and Technology Department of Computer Science Meghalaya India International School of Business and Research Department of Business Analytics Bengaluru India

ISBN: (纸本)9798350379945

Traditional centralized cloud computing is having considerable issues due to the rapid proliferation of mobile internet and Internet of Things applications. These issues include data transfer delays, low efficiency in exploiting available spectrum, and connectivity that is inflexible based on machine type. Driven by the need to overcome these challenges, a new technology is prompting a change in the role of centralized cloud computing to edge devices within networks. Driven by the need to overcome these challenges, a revolutionary technology is prompting a change in the role of centralized cloud computing to edge devices within the networks. Edge computing systems have emerged from a variety of sources, with the goal of reducing latency, improving SE (security and efficiency), and enabling wide connectivity among devices. Given the broad growth and advancement of cloud technologies, the existing cloud computing model is insufficient to address the critical needs for providing services to applications such as smart grid, healthcare systems, and augmented reality. These specifications include low latency, mobility support, and context awareness. Fog and edge computing have developed as technologies that meet these requirements while providing great performance and dependability. Machine learning entails detecting many characteristics such as automated procedures, increased decision support, and diagnostic capabilities within operational settings. This study exposes previous research that focuses on diverse applications of machine learning, identifying potential hazards, and developing solutions for the edge paradigm as a whole. It also underlines the outstanding issues and challenges that may remain in the coming decades. © 2024 ieee.

关键词： Cloud platforms

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Forecasting Tsunami Waves using Regression Trees 8

Forecasting Tsunami Waves using Regression Trees

引用

ieee 8th international conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Cesario, Eugenio Giampa, Salvatore Baglione, Enrico Cordrie, Louise Selva, Jacopo Talia, Domenico Univ Calabria DtoK Lab Relatech Arcavacata Di Rende CS Italy DtoK Lab Relatech Arcavacata Di Rende CS Italy Univ Bologna Bologna Italy INGV Bologna Italy Univ Naples Federico II Naples Italy

ISBN: (纸本)9798350319514

After a seismic event, tsunami early warning systems (TEWSs) try to accurately forecast the maximum height of incident waves at specific target points in front of the coast. The goal is to launch early warnings on locations where the impact of tsunami waves can be destructive, and to refine these forecasts in urgent computing mode in its immediate aftermath, to help organizing potential recovery operations. For improving the accuracy and computational efficiency of classic tsunami forecasting methods based on simulation models, scientists have recently started to exploit machine learning techniques to process pre-computed simulation data, in order to extract tsunami predictive models. However, the proposed approaches, mainly based on neural networks, suffer of high training time and limited model explainability. This paper describes a machine learning approach based on regression trees to model and forecast tsunami evolutions to overtake these issues. The experimental evaluation, performed on a real-world earthquake and tsunami simulation case study, shows that regression trees achieve high forecasting accuracy. Moreover, they provide domain experts with fully-explainable and interpretable models, which are a valuable support for environmental scientists because they describe underlying rules and patterns behind the models and allow for an explicit inspection of their functioning.

关键词： Tsunami forecasting machine learning urgent computing regression trees

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OpenCUBE: Building an Open Source Cloud Blueprint with EPI systems

OpenCUBE: Building an Open Source Cloud Blueprint with EPI S...

引用

29th international conference on Parallel and distributed computing (Euro-Par)

作者： Peng, Ivy Schulz, Martin Haus, Utz-Uwe Prunty, Craig Marcuello, Pedro Danovaro, Emanuele Schieffer, Gabin Wahlgren, Jacob Medeiros, Daniel Friese, Philipp Markidis, Stefano KTH Royal Inst Technol Stockholm Sweden Tech Univ Munich Munich Germany Hewlett Packard Enterprise Zurich Switzerland SiPearl Maisons Laffitte France Semidynamics Barcelona Spain ECMWF Bologna Italy

ISBN: (纸本)9783031488023;9783031488030

OpenCUBE aims to develop an open-source full software stack for Cloud computing blueprint deployed on EPI hardware, adaptable to emerging workloads across the computing continuum. OpenCUBE prioritizes energy awareness and utilizes open APIs, Open Source components, advanced SiPearl Rhea processors, and RISC-V accelerator. The project leverages representative workloads, such as cloud-native workloads and workflows of weather forecast data management, molecular docking, and space weather, for evaluation and validation.

关键词： Open-source Converged HPC and Cloud computing continuum EPI RISC-V

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An Efficient Task Scheduling for Latency Sensitive Tasks in Edge - Cloud computing Environment 15

An Efficient Task Scheduling for Latency Sensitive Tasks in ...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Thotakura, Yaswanth Chowdary Gollapalli, Geetha Siva Srinivas Yarramsetty, Dinesh Manikanta Kasim, Shalom Raja Doppalapudi, Kalyan Kumar Swain, Chinmaya Kumar Srm University - Ap Dept. of Cse Andhra Pradesh India

ISBN: (纸本)9798350370249

With an emphasis on fulfilling deadlines, this study presents an efficient method for workload scheduling in edge-cloud collaborative computing settings. It optimizes task distribution taking into account variables like task arrival rate and resource availability. A genetic algorithm-based approach is suggested to improve the procedure of scheduling further. It makes use of task prioritization, mutation, and solution encoding. To achieve deadline fulfillment and resource optimization in edge-cloud collaborative computing systems, this method successfully tackles the problems of fluctuating user demands and resource constraints. It also provides an effective solution for dynamic task scheduling. © 2024 ieee.

关键词： Earliest Deadline First (EDF) Edge computing First-Come-First-Serve (FCFS) Genetic Algorithm (GA) Solution Encoding

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Optimizing Relay Protection in distributed Generation systems Through Intelligent Electronic Devices Powered by Current Transformers 5

Optimizing Relay Protection in Distributed Generation System...

引用

5th National and the 1st international conference on Applied Research in Electrical Engineering, AREE 2025

作者： Burdukov, Ivan Tashkent Uzbekistan

ISBN: (纸本)9798350377170

The integration of distributed Generation (DG) systems, particularly wind turbine generators (WTGs), into power grids presents significant challenges in protection and coordination due to their unique characteristics. Traditional protection methods, such as overcurrent protection, are often insufficient for generator systems with limited short-circuit current contributions. This paper explores the limitations of conventional relay protection for wind turbines, highlighting issues such as reverse power flow, false tripping, and inadequate selectivity. We propose a solution involving Intelligent Electronic Devices (IEDs) powered by current transformers (CTs) to improve protection functionality, adapt to changing network conditions, and enhance system reliability. The paper further discusses the advantages of IEDs, such as adaptive fault detection, real-time monitoring, and reduced downtime. We present an evaluation of various protection functions for wind turbines, including phase overcurrent, neutral overcurrent, and voltage-based protection, as well as the integration of IEDs with modern protection schemes. Finally, the feasibility of implementing CT-powered IEDs in a distribution network model is examined using the ETAP software suite, offering insights into their performance and impact on DG systems. © 2025 ieee.

关键词： Wind Turbine Generators

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Research on Distribution Network Monitoring and Fault Location Based on Edge computing 13th

Research on Distribution Network Monitoring and Fault Locati...

引用

13th international conference on Frontier computing, FC 2023

作者： Qin, Jia Yu, Daquan Wang, Hexin Heilongjiang University of Technology Heilongjiang Jixi158100 China

ISBN: (纸本)9789819995370

The research of distribution network monitoring and fault location based on edge computing is a research, focusing on the design of low power consumption, high reliability and high fault tolerance distributed systems. The main purpose of this research is to develop a distributed system architecture for monitoring and locating faults in large-scale networks. The architecture consists of three components: fault location component (FLC), data acquisition component (DCC) and data analysis component (DAC). These three components are connected through an intermediate layer called the Service Interface Controller (SIC). SIC provides necessary interfaces between DCC, DAC and FLC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Electric power distribution

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PlanX: A Toolbox for Building and Integrating AI Planning systems 17

PlanX: A Toolbox for Building and Integrating AI Planning Sy...

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17th ieee international Congress on Intelligent and Service-Oriented systems Engineering (CISOSE)

作者： Georgievski, Ilche Univ Stuttgart Serv Comp Dept IAAS Stuttgart Germany

ISBN: (纸本)9798350322392

We introduce the PlanX toolbox to support and promote building and integrating AI planning systems. The prototype toolbox is aimed at researchers and developers who need to build and use advanced AI planning systems to address planning problems. It is also well suited for composing AI planning systems that need to be integrated into larger software architectures. The toolbox offers an initial set of planning services realised using existing tools and our own implementations. The planning services are based on technologies that enable the resulting system to be easily set up and run on different platforms. We show on a use case that building and expanding an AI planning system with the PlanX toolbox is not only possible but also quick. PlanX is released as open-source to encourage its wider use and contributions to the code base.

关键词： AI Planning System Engineering Service-Oriented computing Toolbox

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Chaotic and Transfer Function-Assisted Grey Wolf Optimizer for IoT Task Scheduling in Cloud-of Things

Chaotic and Transfer Function-Assisted Grey Wolf Optimizer f...

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2024 ASU international conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Qasim, Mohammad Sajid, Mohammad Department of Computer Science Aligarh Muslim University Aligarh India

ISBN: (纸本)9798350372229

The amalgamation of Cloud computing and Internet-of-Things (IoT), i.e., Cloud-of-Things (CoT), has emerged as one of the indispensable technologies in the IT and business world. The success of CoT depends on the efficient services provided by cloud computing to the requests generated by IoT devices. The efficient management of IoT services on cloud virtualized resources, an NP-hard problem, is the major component in developing trust and reliability among IoT users. This research proposes a novel chaotic and transfer functionassisted modified grey wolf optimizer (MGWO) algorithm to schedule IoT requests on virtual machines in cloud computing. The proposed MGWO algorithm utilizes a transfer function to convert the wolf into the solution and a chaotic circle map to improve the initial population's diversity, which leads to faster convergence and better exploitation and exploration. The MGWO algorithm's performance is superior to the generic version of the GWO algorithm and its variants. © 2024 ieee.

关键词： Chaotic Map Cloud computing Grey Wolf Optimizer Task Scheduling Transfer Function

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