Network Intrusion Detection using Deep Learning: A Feature Learning Approach (SpringerBriefs on Cyber Security Systems and Networks)

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. Front Matter ....Pages i-xvii Introduction (Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja)....Pages 1-4 Intrusion Detection Systems (Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja)....Pages 5-11 Classical Machine Learning and Its Applications to IDS (Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja)....Pages 13-26 Deep Learning (Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja)....Pages 27-34 Deep Learning-Based IDSs (Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja)....Pages 35-45 Deep Feature Learning (Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja)....Pages 47-68 Summary and Further Challenges (Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja)....Pages 69-70 Back Matter ....Pages 71-79