This study examines the relationship between Quality of Service (QoS) andsecurity in Wi-Fi networks. Using a detailed dataset consisting of QoS metrics of file transfers under various security configurations, we anal...
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Accurately identifying concealed wildlife, including arachnids, serpents, and arthropods, is paramount for proficient agricultural management. This study introduces a deep learning approach utilizing transformers for ...
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In this article, we proposed a hybrid model comprising a genetic algorithm and a decision tree for secure data exchange in autonomous vehicles (AVs). The integration of a genetic algorithm enables systematic feature o...
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PDF malware is a significant threat to computersecurity. The purpose of this study is to introduce a new approach for improving the security of PDF readers. The method utilizes transfer learning by leveraging existin...
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In the past decade, the proliferation of Internet of Things (IoT) applications and implementations has exhibited exponential growth, concurrent with advancements in technologies such as machine learning and cloud comp...
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Using IoT devices in smart homes brings benefits andsecurity dangers. This study extensively examines various intrusion detection methods within smart home environments. It also suggests a novel hybrid intrusion dete...
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Many mobile applications have resorted to deep neural networks (DNNs) because of their strong inference capabilities. Since both input data and DNN architectures could be sensitive, there is an increasing demand for s...
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ISBN:
(纸本)9798400700989
Many mobile applications have resorted to deep neural networks (DNNs) because of their strong inference capabilities. Since both input data and DNN architectures could be sensitive, there is an increasing demand for secure DNN execution on mobile devices. Towards this end, hardware-based trusted execution environments on mobile devices (mobile TEEs), such as ARM TrustZone, have recently been exploited to execute CNN securely. However, running entire DNNs on mobile TEEs is challenging as TEEs have stringent resource and performance constraints. In this work, we develop a novel mobile TEE-based security framework that can efficiently execute the entire DNN in a resource-constrained mobile TEE with minimal inference time overhead. Specifically, we propose a progressive pruning to gradually identify and remove the redundant neurons from a DNN while maintaining a high inference accuracy. Next, we develop a memory optimization method to deallocate the memory storage of the pruned neurons utilizing the low-level programming technique. Finally, we devise a novel adaptive partitioning method that divides the pruned model into multiple partitions according to the available memory in the mobile TEE and loads the partitions into the mobile TEE separately with a minimal loading time overhead. Our experiments with various DNNs and open-source datasets demonstrate that we can achieve 2-30 times less inference time with comparable accuracy compared to existing approaches securing entire DNNs with mobile TEE.
At present, the new era needs a method that can accurately guide maritime personnel to evaluate ship network security, so this paper constructs a ship network security evaluation method to analyze different indicators...
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With the advancing technology, it becomes difficult to cope up with novel trends and configurations. Similarly, it is difficult to secure the systems against each emerging threat. With this the loopholes in convention...
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This research aims to develop a quantum encrypted messaging application that uses the principles of quantum mechanics to provide secure and privacy communication between users This project focuses on using quantum enc...
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