Due to developments in technologies like Cloud Computing (CC), the Internet of Things (IoT), etc., the data volume transmitted across communication infrastructures has skyrocketed recently. In order to make network sy...
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At present, deep learning is being used to compute data related to medical images with the purpose of improving disease-related research. Deep learning based medical image analysis includes various important tasks suc...
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By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and loweri...
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This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable model (EPDTNet + -Em), designed to improve the detection and classification of abnormalities in medical...
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Forecasting of energy is a crucial component in overcoming the challenges of smart grid mechanisms, which include functions such as demand-side management, load reduction, and desirable allocation. The most significan...
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In the ever-changing field of cybersecurity, conventional defenses that rely on boundaries are no longer adequate to safeguard against sophisticated and persistent assaults. The Zero Trust Architecture (ZTA) paradigm,...
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machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the a...
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machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an assumption is not always valid as training data might include measurement samples that are incorrectly labeled as benign, namely, adversarial data poisoning samples, which have not been detected before. Neglecting such an aspect makes detectors susceptible to data poisoning. Our investigations revealed that detection rates (DRs) of existing detectors significantly deteriorate by up to 9-29% when subject to data poisoning in generalized and topology-specific settings. Thus, we propose a generalized graph neural network-based anomaly detector that is robust against FDIAs and data poisoning. It requires only benign datasets for training and employs an autoencoder with Chebyshev graph convolutional recurrent layers with attention mechanism to capture the spatial and temporal correlations within measurement data. The proposed convolutional recurrent graph autoencoder model is trained and tested on various topologies (from 14, 39, and 118-bus systems). Due to such factors, it yields stable generalized detection performance that is degraded by only 1.6-3.7% in DR against high levels of data poisoning and unseen FDIAs in unobserved topologies. Impact Statement-Artificial Intelligence (AI) systems are used in smart grids to detect cyberattacks. They can automatically detect malicious actions carried out bymalicious entities that falsifymeasurement data within power grids. Themajority of such systems are data-driven and rely on labeled data for model training and testing. However, datasets are not always correctly labeled since malicious entities might be carrying out cyberattacks without being detected, which leads to training on mislabeled datasets. Such actions might degrade the d
The cybersecurity concerns of intelligent microgrids are thoroughly investigated in this scholarly work. The intricate link that exists between the cyber grid and smart grid operating processes is examined, exposing t...
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This paper describes and evaluates five different crawling algorithms that we have implemented within our evaluation framework: Shark search, Priority Based Focused Crawler,Naive Bayes,Breadth-First, Depth-First and c...
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technological developments in energy production and use are crucial to the shift to a future with fewer emissions and better health. Vehicle technology is impacted by a number of variables, including pollution-induced...
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