Network Intrusion Detection using Deep Learning

Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja

Informasi Dasar

21.21.1537
005.8
Buku - Elektronik (E-Book)
4

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.

Subjek

COMPUTER SECURITY
 

Katalog

Network Intrusion Detection using Deep Learning
978-981-13-1444-5
92p.: pdf file.; 2 MB
Inggris

Sirkulasi

Rp. 0
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Tidak

Pengarang

Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja
Perorangan
 
 

Penerbit

Springer International Publishing
New York
2018

Koleksi

Kompetensi

 

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