Cloud computing is increasingly becoming a desirable and foundational element in international enterprise computing. There are many companies which design, develop, and offer cloud technologies. However, cloud provide...
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In this paper, we apply General Order Statistics (GOS) theorem for the antenna selection in dual-hop amplify-forward fixed-gain relay transmission. The antenna selection is employed at multi-antenna relay terminal for...
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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
A novel and rapidly growing area of research concerns data-intensive applications and the technical challenges that accompany it. One of those challenges is developing approaches and mechanisms that render high perfor...
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The performance of a dual-hop amplify-and forward relay system is analyzed in terms of outage probability and average symbol error rate. The source-relay and relay-destination channels experience mixed fading distribu...
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In this paper, we propose to combine active solid state drives and reconfigurable FPGAs into a storage-compute node to use as a building block in a distributed, high performance computation platform for data intensive...
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作者:
Zhao, RanChen, LiangHu, JunBagci, Hakan
Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division Thuwal23955 Saudi Arabia
School of Electronic Science and Engineering Chengdu610056 China
A multi-trace surface integral equation (MT-SIE) solver is proposed to analyze electromagnetic field interactions on graphene-based devices. This solver decomposes the computation domain into an exterior subdomain (th...
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In this paper we will discuss the experience of using simple robotics projects to introduce first year students to engineering. The key learning objectives and tools used to implement them and the evaluation results w...
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Organic semiconductor detectors have always been in active research interest of researchers due to its low fabrication cost. Vertical organic detectors have been studied in the past but not much of the works have been...
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The increased use of small satellites in commercial, defense, and research industries reinforces the need for efficient and reliable communication subsystems. Compact, space rated, communication subsystems present the...
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