| 书目名称 | Network Intrusion Detection using Deep Learning |
| 副标题 | A Feature Learning A |
| 编辑 | Kwangjo Kim,Muhamad Erza Aminanto,Harry Chandra Ta |
| 视频video | |
| 概述 | Surveys deep learning-based IDSs.Suggests future directions for IDS research.Describes how to apply deep learning in IDS.Discusses learning for better attack detection |
| 丛书名称 | SpringerBriefs on Cyber Security Systems and Networks |
| 图书封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2018 |
| 关键词 | Security and Privacy; Intrusion Detection System; Detection of unknown attacks; Anomaly detection; Deep |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-13-1444-5 |
| isbn_softcover | 978-981-13-1443-8 |
| isbn_ebook | 978-981-13-1444-5Series ISSN 2522-5561 Series E-ISSN 2522-557X |
| issn_series | 2522-5561 |
| copyright | The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2018 |
发表于 2025-3-21 19:42:00
