Deep Learning Intrusion Detection (87 results)
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Network Intrusion Detection With Deep Learning : Feature Learning Approach
Kim, Kwangjo; Aminanto, Muhamad Erza; Tanuwidjaja, Harry Chandra
Language: English
Published by Springer 2018
Series: SpringerBriefs on Cyber Security Systems and Networks, Book 6 of 10. Book 6 of 10 - SpringerBriefs on Cyber Security Systems and Networks
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Network Intrusion Detection using Deep Learning: A Feature Learning Approach (SpringerBriefs on Cyber Security Systems and Networks)
Kim, Kwangjo; Aminanto, Muhamad Erza; Tanuwidjaja, Harry Chandra
Language: English
Published by Springer 2018
Series: SpringerBriefs on Cyber Security Systems and Networks, Book 6 of 10. Book 6 of 10 - SpringerBriefs on Cyber Security Systems and Networks
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Network Intrusion Detection With Deep Learning : Feature Learning Approach
Kim, Kwangjo; Aminanto, Muhamad Erza; Tanuwidjaja, Harry Chandra
Language: English
Published by Springer 2018
Series: SpringerBriefs on Cyber Security Systems and Networks, Book 6 of 10. Book 6 of 10 - SpringerBriefs on Cyber Security Systems and Networks
- Softcover
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Network Intrusion Detection using Deep Learning: A Feature Learning Approach (SpringerBriefs on Cyber Security Systems and Networks)
Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja
Language: English
Published by Springer 2018-10-02 2018
Series: SpringerBriefs on Cyber Security Systems and Networks, Book 6 of 10. Book 6 of 10 - SpringerBriefs on Cyber Security Systems and Networks
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Network Intrusion Detection With Deep Learning : Feature Learning Approach
Kim, Kwangjo; Aminanto, Muhamad Erza; Tanuwidjaja, Harry Chandra
Language: English
Published by Springer 2018
Series: SpringerBriefs on Cyber Security Systems and Networks, Book 6 of 10. Book 6 of 10 - SpringerBriefs on Cyber Security Systems and Networks
- Softcover
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Network Intrusion Detection With Deep Learning: Feature Learning Approach
Kim, Kwangjo; Aminanto, Muhamad Erza; Tanuwidjaja, Harry Chandra
Language: English
Published by Springer 2018
Series: SpringerBriefs on Cyber Security Systems and Networks, Book 6 of 10. Book 6 of 10 - SpringerBriefs on Cyber Security Systems and Networks
- Softcover
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Condition: New. 2018th edition NO-PA16APR2015-KAP.
Network Intrusion Detection With Deep Learning : Feature Learning Approach
Kim, Kwangjo; Aminanto, Muhamad Erza; Tanuwidjaja, Harry Chandra
Language: English
Published by Springer 2018
Series: SpringerBriefs on Cyber Security Systems and Networks, Book 6 of 10. Book 6 of 10 - SpringerBriefs on Cyber Security Systems and Networks
- Softcover
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Network Intrusion Detection With Deep Learning: Feature Learning Approach
Kim, Kwangjo/ Aminanto, Muhamad Erza/ Tanuwidjaja, Harry Chandra
Language: English
Published by Springer-Verlag New York Inc 2018
Series: SpringerBriefs on Cyber Security Systems and Networks, Book 6 of 10. Book 6 of 10 - SpringerBriefs on Cyber Security Systems and Networks
- Softcover
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Taschenbuch. Condition: Neu. Deep Learning Based Intrusion Detection Framework in Industrial IoT | Raghavender K V (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206739449 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]d…e | Anbieter: preigu.
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Taschenbuch. Condition: Neu. Intrusion Detection System based on deep learning | Junior Momo Ziazet | Taschenbuch | Englisch | 2023 | Our Knowledge Publishing | EAN 9786205985717 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Language: English
Published by Springer 2018
Series: SpringerBriefs on Cyber Security Systems and Networks, Book 6 of 10. Book 6 of 10 - SpringerBriefs on Cyber Security Systems and Networks
- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Network Intrusion Detection using Deep Learning | A Feature Learning Approach | Kwangjo Kim (u. a.) | Taschenbuch | SpringerBriefs on Cyber Security Systems and Networks | xvii | Englisch | 2018 | Springer | EAN 9789811314438 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 1…7, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Springer, Springer 2018
Series: SpringerBriefs on Cyber Security Systems and Networks, Book 6 of 10. Book 6 of 10 - SpringerBriefs on Cyber Security Systems and Networks
- Softcover
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular,… it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity.
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Hardcover. Condition: new. Hardcover. Comprehensive resource exploring deep learning techniques for intrusion detection in various applications such as cyber physical systems and IoT networks Deep Learning for Intrusion Detection provides a practical guide to understand the challenges of intrusion detection in various applicatio…n areas and how deep learning can be applied to address those challenges. It begins by discussing the basic concepts of intrusion detection systems (IDS) and various deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). Later chapters cover timely topics including network communication between vehicles and unmanned aerial vehicles. The book closes by discussing security and intrusion issues associated with lightweight IoTs, MQTT networks, and Zero-Day attacks. The book presents real-world examples and case studies to highlight practical applications, along with contributions from leading experts who bring rich experience in both theory and practice. Deep Learning for Intrusion Detection includes information on: Types of datasets commonly used in intrusion detection research including network traffic datasets, system call datasets, and simulated datasets The importance of feature extraction and selection in improving the accuracy and efficiency of intrusion detection systems Security challenges associated with cloud computing, including unauthorized access, data loss, and other malicious activities Mobile Adhoc Networks (MANETs) and their significant security concerns due to high mobility and the absence of a centralized authority Deep Learning for Intrusion Detection is an excellent reference on the subject for computer science researchers, practitioners, and students as well as engineers and professionals working in cybersecurity. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Taschenbuch. Condition: Neu. Optimized Deep Learning for Network Intrusion Detection | Bhushan Deore | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208440213 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de |…Anbieter: preigu.
- Hardcover
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Hardback. Condition: New. Comprehensive resource exploring deep learning techniques for intrusion detection in various applications such as cyber physical systems and IoT networks Deep Learning for Intrusion Detection provides a practical guide to understand the challenges of intrusion detection in various application areas and…how deep learning can be applied to address those challenges. It begins by discussing the basic concepts of intrusion detection systems (IDS) and various deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). Later chapters cover timely topics including network communication between vehicles and unmanned aerial vehicles. The book closes by discussing security and intrusion issues associated with lightweight IoTs, MQTT networks, and Zero-Day attacks. The book presents real-world examples and case studies to highlight practical applications, along with contributions from leading experts who bring rich experience in both theory and practice. Deep Learning for Intrusion Detection includes information on: Types of datasets commonly used in intrusion detection research including network traffic datasets, system call datasets, and simulated datasets The importance of feature extraction and selection in improving the accuracy and efficiency of intrusion detection systems Security challenges associated with cloud computing, including unauthorized access, data loss, and other malicious activities Mobile Adhoc Networks (MANETs) and their significant security concerns due to high mobility and the absence of a centralized authority Deep Learning for Intrusion Detection is an excellent reference on the subject for computer science researchers, practitioners, and students as well as engineers and professionals working in cybersecurity.
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- Softcover
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Taschenbuch. Condition: 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 Landst…r. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Taschenbuch. Condition: Neu. Intrusion Detection | Map Reduce Based Deep Learning In Big Data Environment | Gunavathi Ramasamy (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206158851 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]…preigu[dot]de | Anbieter: preigu.
