Network Intrusion Detection using Deep Learning [electronic resource] : A Feature Learning Approach /
Kim, Kwangjo.author.(orcid)0000-0002-8922-6203https://orcid.org/0000-0002-8922-6203aut
Aminanto, Muhamad Erza.author.
Tanuwidjaja, Harry Chandra.author.(orcid)0000-0003-2668-559Xhttps://orcid.org/0000-0003-2668-559Xaut
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.
Chapter 1 Introduction -- Chapter 2 Intrusion Detection Systems -- Chapter 3 Classical Machine Learning and Its Applications to IDS -- Chapter 4 Deep Learning -- Chapter 5 Deep Learning-based IDSs -- Chapter 6 Deep Feature Learning -- Chapter 7 Summary and Further Challenges.
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.
Data protection.
Artificial intelligence.
Computer security.
Wireless communication systems.
Mobile communication systems.
Big data.
Data mining.
Security.
Artificial Intelligence.
Systems and Data Security.
Wireless and Mobile Communication.
Big Data.
Data Mining and Knowledge Discovery.