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.

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.

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.

I COMMUNICATIONS NETWORKS AND SYSTEMS Networking Future Internet Future Networks QoS QoE and Resource Management Optical Networks Wireless, Mobile, Adhoc and Sensor Networks Ubiquitous Networks Network Security Multimedia Networking etc Communication Systems Coding and Information Theory Wireless, UWB, Ultrasonic Communications Satellite Communications Other Emerging Technologies Network Coding, Software Defined Radio, Cognitive Radio etc II SIGNAL PROCESSING & APPLICATIONS Signal, Image, Audio, Video Processing, Analysis and Applications Pattern Recognition Biomedical Signal Processing and Analysis Signal Filtering, Detection and Estimation Statistical Signal Processing and Modeling Ambient Intelligence Computer Vision and Audition III OPTICAL COMMUNICATIONS AND NETWORKING Design and Management of Optical Networks Optical Networks Performance Modeling Optical Networks Control and Management Optical Modulation and Signal Processing Reliable Optical Netwo

With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavi

The Ninth International Conference on Computing, Communication and Networking Technologies (9th ICCCNT 2018) aims to provide a forum that brings together International researchers from academia and practitioners in the industry to meet and exchange ideas and recent research work on all aspects of Information and Communication Technologies Following the great success of ICCCNT 08, ICCCNT 10, ICCCNT 12, ICCCNT 13, ICCCNT 14, ICCCNT 15,ICCCNT 16 and ICCCNT 17 The ninth edition of the event, ICCCNT 17, will be held in IISc, Bangalore on July 10 12, 2018 The conference will consist of keynote speeches, technical sessions, and exhibition The technical sessions will present original and fundamental research advances, and the workshops will focus on hot topics in Information and Communication Engineering Experts from NASA, MIT, Japan will give key note speeches

This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.