Network Intrusion Detection using Deep Learning: A Feature Learning Approach - Muhamad Erza Aminanto,Harry Chandra Tanuwidjaja,Kwangjo Kim
-40% koodiga BOOKS
Saadetis 12-18 tööpäeva jooksul
30-päevane tagastamisõigus
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 le ... Täielik kirjeldus
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Kirjeldus
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.
Lisateave
| Autor | Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja, Kwangjo Kim |
|---|---|
| Kirjastaja | Springer Nature Singapore |
| Series | SpringerBriefs on Cyber Security Systems and Networks |
| Väljalaskeaasta | 2018 |
| Kaanetüüp | Pehme kaanega |
| EAN | 9789811314438 |