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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Network Intrusion Detection Using Deep Learning: A Feature Learning Approach

Network Intrusion Detection Using Deep Learning: A Feature Learning Approach

Kim, Kwangjo
Aminanto, Muhamad Erza
Tanuwidjaja, Harry Chandra

Paperback

Series: Springerbriefs on Cyber Security Systems and Networks

Technology & EngineeringGeneral ComputersComputer Security

ISBN10: 9811314438
ISBN13: 9789811314438
Publisher: Springer Nature
Published: Oct 2 2018
Pages: 79
Weight: 0.33
Height: 0.21 Width: 6.14 Depth: 9.21
Language: English

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.

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