Applied Sciences, Free Full-Text

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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
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
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Applied Sciences, Free Full-Text
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Applied Sciences, Free Full-Text
Science, health and medical journals, full text articles and books.
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 An Open Access Journal from MDPI
Applied Sciences, Free Full-Text
Applied Sciences, Free Full-Text
Applied Sciences, Free Full-Text
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