Intrusion Detection Systems, Beata Akselsen

Автор: Kwangjo Kim; Muhamad Erza Aminanto; Harry Chandra
Название: Network Intrusion Detection using Deep Learning
ISBN: 9811314438 ISBN-13(EAN): 9789811314438
Издательство: Springer
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Цена: 51230.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.

Автор: ?lvaro Herrero; Emilio Corchado
Название: Mobile Hybrid Intrusion Detection
ISBN: 3642423418 ISBN-13(EAN): 9783642423413
Издательство: Springer
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Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It has led to the design of MOVICAB-IDS (MObile VIsualisation Connectionist Agent-Based IDS), a novel Intrusion Detection System (IDS), which is comprehensively described in this book.This novel IDS combines different AI paradigms to visualise network traffic for ID at packet level.