Network Intrusion Detection using Deep Learning [electronic resource] : A Feature Learning Approach / by Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja.

Por: Kim, Kwangjo [author.]Colaborador(es): Aminanto, Muhamad Erza [author.] | Tanuwidjaja, Harry Chandra [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries SpringerBriefs on Cyber Security Systems and NetworksEditor: Singapore : Springer Singapore : Imprint: Springer, 2018Edición: 1st ed. 2018Descripción: XVII, 79 p. 30 illus., 11 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789811314445Tema(s): Data protection | Artificial intelligence | Computer security | Wireless communication systems | Mobile communication systems | Big data | Data mining | Security | Artificial Intelligence | Systems and Data Security | Wireless and Mobile Communication | Big Data | Data Mining and Knowledge DiscoveryFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 005.8 Clasificación LoC:QA76.9.A25Recursos en línea: Libro electrónicoTexto
Contenidos:
Chapter 1 Introduction -- Chapter 2 Intrusion Detection Systems -- Chapter 3 Classical Machine Learning and Its Applications to IDS -- Chapter 4 Deep Learning -- Chapter 5 Deep Learning-based IDSs -- Chapter 6 Deep Feature Learning -- Chapter 7 Summary and Further Challenges.
En: Springer Nature eBookResumen: 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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Chapter 1 Introduction -- Chapter 2 Intrusion Detection Systems -- Chapter 3 Classical Machine Learning and Its Applications to IDS -- Chapter 4 Deep Learning -- Chapter 5 Deep Learning-based IDSs -- Chapter 6 Deep Feature Learning -- Chapter 7 Summary and Further Challenges.

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.

UABC ; Temporal ; 01/01/2021-12/31/2023.

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