Atnaujintas knygų su minimaliais defektais pasiūlymas! Naršykite ČIA >>
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.
| Autorius: | Kwangjo Kim, Harry Chandra Tanuwidjaja, Muhamad Erza Aminanto, |
| Serija: | SpringerBriefs on Cyber Security Systems and Networks |
| Leidėjas: | Springer Nature Singapore |
| Išleidimo metai: | 2018 |
| Knygos puslapių skaičius: | 100 |
| ISBN-10: | 9811314438 |
| ISBN-13: | 9789811314438 |
| Formatas: | 235 x 155 x 6 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Network Intrusion Detection using Deep Learning: A Feature Learning Approach“
