Network Intrusion Detection using Deep Learning: A Feature Learning Approach eBook

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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.

This is a digital product.

Network Intrusion Detection using Deep Learning: A Feature Learning Approach is written by Kwangjo Kim; Muhamad Erza Aminanto; Harry Chandra Tanuwidjaja and published by Springer. The Digital and eTextbook ISBNs for Network Intrusion Detection using Deep Learning are 9789811314445, 9811314446 and the print ISBNs are 9789811314438, 9811314438.