A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect ...
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With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL). In this paper, we propose a general framework that unifies...
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In this paper, we address the complex problem of detecting overlapping speech segments, a key challenge in speech processing with applications in speaker diarization, automatic transcription, and multi-speaker recogni...
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This paper proposes an intelligent dynamic modeling method for strip rolling process. Actuators of a cold rolling mill perform actions, including work roll bending, intermediate roll bending, and roll gap tilting, to ...
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With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use ...
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With the integration of distributed energy resources such as roof-top solar panels and wind turbines into the grid, power generation can surpass demand-generation and thus, giving rise to the negative pricing, especia...
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This paper explores the capability of transfer learning for the correct segmentation of pathological systems in X-ray pictures. Switch mastering is a way in gadget mastering wherein understanding received from a sourc...
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Machine learning has been adopted for efficient cooperative spectrum sensing. However, it incurs an additional security risk due to attacks leveraging adversarial machine learning to create malicious spectrum sensing ...
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ISBN:
(数字)9798350383508
ISBN:
(纸本)9798350383515
Machine learning has been adopted for efficient cooperative spectrum sensing. However, it incurs an additional security risk due to attacks leveraging adversarial machine learning to create malicious spectrum sensing values to deceive the fusion center, called adversarial spectrum attacks. In this paper, we propose an efficient framework for detecting adversarial spectrum attacks. Our design leverages the concept of the distance to the decision boundary (DDB) observed at the fusion center and compares the training and testing DDB distributions to identify adversarial spectrum attacks. We create a computationally efficient way to compute the DDB for machine learning based spectrum sensing systems. Experimental results based on realistic spectrum data show that our method, under typical settings, achieves a high detection rate of up to 99% and maintains a low false alarm rate of less than 1%. In addition, our method to compute the DDB based on spectrum data achieves 54%–64% improvements in computational efficiency over existing distance calculation methods. The proposed DDB-based detection framework offers a practical and efficient solution for identifying malicious sensing values created by adversarial spectrum attacks.
Bangladesh is mostly an agricultural nation. Agriculture is a time-consuming and labor-intensive operation. Farmers must labor long and hard hours to produce a harvest. Farmers must overcome various obstacles to reap ...
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There are increasing concerns about power quality disturbances (PQDs) at many phases of energy generation, transformation, distribution, and consumption due to the increasing interconnection of various energy systems....
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