Applied Sciences, Free Full-Text

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Descrição

The rapid growth of the Internet of Things (IoT) has led to an increased automation and interconnectivity of devices without requiring user intervention, thereby enhancing the quality of our lives. However, the security of IoT devices is a significant concern as they are vulnerable to cyber-attacks, which can cause severe damage if not detected and resolved in time. To address this challenge, this study proposes a novel approach using a combination of deep learning and three-level algorithms to detect attacks in IoT networks quickly and accurately. The Bot-IoT dataset is used to evaluate the proposed approach, and the results show significant improvements in detection performance compared to existing methods. The proposed approach can also be extended to enhance the security of other IoT applications, making it a promising contribution to the field of IoT security.
Applied Sciences, Free Full-Text
SFU Applied Sciences
Applied Sciences, Free Full-Text
Applied Sciences An Open Access Journal from MDPI
Applied Sciences, Free Full-Text
Advanced Science - Wiley Online Library
Applied Sciences, Free Full-Text
Applied Sciences, Free Full-Text
Applied Sciences, Free Full-Text
Applied Sciences An Open Access Journal from MDPI
Applied Sciences, Free Full-Text
Applied Sciences An Open Access Journal from MDPI
Applied Sciences, Free Full-Text
Applied sciences Stock Photos, Royalty Free Applied sciences Images
Applied Sciences, Free Full-Text
Applied sciences Stock Photos, Royalty Free Applied sciences Images
Applied Sciences, Free Full-Text
Applied Sciences An Open Access Journal from MDPI
Applied Sciences, Free Full-Text
Abstract Word Cloud For Bachelor Of Applied Science With Related Tags And Terms Stock Photo, Picture and Royalty Free Image. Image 17024229.
Applied Sciences, Free Full-Text
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Applied Sciences, Free Full-Text
Applied Sciences An Open Access Journal from MDPI
Applied Sciences, Free Full-Text
Applied Sciences An Open Access Journal from MDPI
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