Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 2Machine Learning Algorithms Against Hacking Attack and Detection Success Comparison(Institute of Electrical and Electronics Engineers Inc., 2020-09-15) Yavuz, Levent; Soran, Ahmet; Onen, Ahmet; Muyeen, S. M.Power system protection units has got enormous importance with the growing risk of cyber-attacks. To create sustainable and well protected system, power system data must be healthy. For that purpose, many machine learning applications have been developed and used for bad data detection. However, each method has got different detection and application process. Methods has superiority over other methods. Although, an algorithm can detect some injections easily, same algorithm can be fail when injection type changed. So methods have got different success results when the injection types changed. For that reason, different injection types are applied on power system IEEE 14 bus system via created special hacking algorithm. PSCAD and python linkage has been used for simulation and detection parts. 3 different injection types created and applied on the system and five different most popular algorithms (SVM, k- NN, LDA, NB, LR) tested. Each algorithm's performances are compared and evaluated. © 2020 Elsevier B.V., All rights reserved.Article Citation - Scopus: 49EdgeAISim: A Toolkit for Simulation and Modelling of AI Models in Edge Computing Environments(Elsevier Ltd, 2024-02) Nandhakumar, Aadharsh Roshan; Baranwal, Ayush; Choudhary, Priyanshukumar; Golec, Muhammed; Gill, Sukhpal SinghTo meet next-generation Internet of Things (IoT) application demands, edge computing moves processing power and storage closer to the network edge to minimize latency and bandwidth utilization. Edge computing is becoming increasingly popular as a result of these benefits, but it comes with challenges such as managing resources efficiently. Researchers are utilising Artificial Intelligence (AI) models to solve the challenge of resource management in edge computing systems. However, existing simulation tools are only concerned with typical resource management policies, not the adoption and implementation of AI models for resource management, especially. Consequently, researchers continue to face significant challenges, making it hard and time-consuming to use AI models when designing novel resource management policies for edge computing with existing simulation tools. To overcome these issues, we propose a lightweight Python-based toolkit called EdgeAISim for the simulation and modelling of AI models for designing resource management policies in edge computing environments. In EdgeAISim, we extended the basic components of the EdgeSimPy framework and developed new AI-based simulation models for task scheduling, energy management, service migration, network flow scheduling, and mobility support for edge computing environments. In EdgeAISim, we have utilized advanced AI models such as Multi-Armed Bandit with Upper Confidence Bound, Deep Q-Networks, Deep Q-Networks with Graphical Neural Network, and Actor-Critic Network to optimize power usage while efficiently managing task migration within the edge computing environment. The performance of these proposed models of EdgeAISim is compared with the baseline, which uses a worst-fit algorithm-based resource management policy in different settings. Experimental results indicate that EdgeAISim exhibits a substantial reduction in power consumption, highlighting the compelling success of power optimization strategies in EdgeAISim. The development of EdgeAISim represents a promising step towards sustainable edge computing, providing eco-friendly and energy-efficient solutions that facilitate efficient task management in edge environments for different large-scale scenarios. © 2023 Elsevier B.V., All rights reserved.