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…mehr
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.
Produktdetails
Produktdetails
SpringerBriefs on Cyber Security Systems and Networks
Professor Kwangjo Kim, an influential figure in cryptography, earned his B.Sc. and M.Sc. from Yonsei University and a Ph.D. from Yokohama National University. He worked at ETRI from 1979 to 1997 and held visiting professorships at prestigious institutions like MIT and UCSD. After his retirement from KAIST in 2021, where he had served since 1998, he became President of the International Research Institute for Cyber Security (IRCS) and remains an Emeritus Professor at KAIST. Professor Kim has been a key contributor to the global cryptographic community, notably serving as a board member of the IACR, chairing the Asiacrypt Steering Committee, and organizing multiple high-profile conferences. Honored as the first Korean IACR Fellow, he co-authored key texts on deep learning and privacy-preserving technologies and was recognized among Stanford's Top 2% of Scientists in 2023. He has significantly advanced post-quantum cryptography with the development of the SOLMAE signature scheme and boasts an H-index of 48 with over 10,000 citations. His research spans cryptography, cybersecurity, and applications, with numerous patents to his name.Currently, appointment as, Adjunct Faculty at Cleveland State Univ, Ohio, USA from FY2025 to FY2028.
Inhaltsangabe
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 Challenges.
