9786205527962 - deep learning: deep learning approaches for intrusion detection and attack severity classification in iot network di madhu, bhukya; venu gopalachari, m (5 risultati)
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Deep Learning | Deep Learning Approaches for Intrusion Detection and Attack Severity Classification in IOT Network | Bhukya Madhu (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786205527962 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Lands…tr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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, art…ificial 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. 168 pp. Englisch.
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 techn.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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, artific…ial 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 168 pp. Englisch.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 80,86
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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, artifici…al 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.
