基于深度学习的网络入侵检测：特征学习方法

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

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.

这本书介绍了使用最新深度学习方法的入侵检测系统（IDSs）的最新进展。它还提供了经典机器学习和最新深度学习发展的系统性概述。特别是，它讨论了不同类别的生成、判别和对抗网络在IDS中的应用。此外，它基于基准测试数据集比较了各种基于深度学习的IDS。这本书还提出了两种新的特征学习模型：深层特征提取与选择（D-FES）以及完全无监督IDS。书的最后部分呈现了一些进一步的挑战和研究方向。提供了一种关于基于深度学习的IDS的全面概述，这本书是本科和研究生学生、以及对深度学习和入侵检测感兴趣的学者和实践者的重要参考资源。此外，比较各种深度学习应用有助于读者获得机器学习的基本理解，并激发在IDS和其他网络安全领域中的应用灵感。

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