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Deep Learning. Deep Learning Approaches for Intrusion Detection and Attack Severity Classification in IOT Network
В наличии
Местонахождение: Алматы | Состояние экземпляра: новый |
Бумажная
версия
версия
Автор: Bhukya Madhu and M Venu Gopalachari
ISBN: 9786205527962
Год издания: 1905
Формат книги: 60×90/16 (145×215 мм)
Количество страниц: 168
Издательство: LAP LAMBERT Academic Publishing
Цена: 46404 тг
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Сферы деятельности:Код товара: 715843
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Аннотация: The Internet of things (IoT) has gained more attention in recent years because of its ubiquitous operations, connectivity, methods of communication, and intelligent decisions to evoke activities from various devices. Therefore, artificial intelligence techniques have been integrated into all aspects of the Internet of Things and making life more comfortable in various ways. A novel deep learning model named Device-based Intrusion Detection System (DIDS) was proposed in the second phase. This DIDS learning model incorporates the prediction of unknown attacks to handle the computational overhead in large networks and increase the throughput with a low false alarm rate. Our proposed algorithm has been evaluated with standard algorithms, and the results show that it detects attacks earlier than standard algorithms. The computational time has also been reduced, and 99% of accuracy has been achieved in detecting the attacks.
Ключевые слова: IoT, Device based Intrusion Detection System, Network Disruption, Machine Learning, Deep Learning
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