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English | PDF,EPUB | 2018 | 92 Pages | ISBN : 9811314438 | 8.74 MB​

Network Intrusion Detection Using Deep Learning A Feature Learning Approach (Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja) (2018) English
Catergory: Computer Technology, Nonfiction

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.

Contents of Download:
9811314438.epub (Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja) (2018) (6.68 MB)
9811314438.pdf (Kwangjo Kim) (2018) (2.06 MB)

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️ Network Intrusion Detection Using Deep Learning A Feature Learning Approach (8.74 MB)
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