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Author: Kwangjo Kim Publisher: Springer ISBN: 9789811314438 Category : Computers Languages : en Pages : 79
Book Description
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.
Author: Kwangjo Kim Publisher: Springer ISBN: 9789811314438 Category : Computers Languages : en Pages : 79
Book Description
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.
Author: Kwangjo Kim Publisher: Springer ISBN: 9811314446 Category : Computers Languages : en Pages : 79
Book Description
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.
Author: Kwangjo Kim Publisher: ISBN: 9789811314452 Category : Computer security Languages : en Pages :
Book Description
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.
Author: IEEE Staff Publisher: ISBN: 9781509017829 Category : Languages : en Pages :
Book Description
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
Author: Dhruba Kumar Bhattacharyya Publisher: CRC Press ISBN: 146658209X Category : Computers Languages : en Pages : 366
Book Description
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 behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents machine learning techniques in depth to help you more effectively detect and counter network intrusion. In this book, you’ll learn about: Network anomalies and vulnerabilities at various layers The pros and cons of various machine learning techniques and algorithms A taxonomy of attacks based on their characteristics and behavior Feature selection algorithms How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance Important unresolved issues and research challenges that need to be overcome to provide better protection for networks Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.
Author: IEEE Staff Publisher: ISBN: 9781728109466 Category : Languages : en Pages :
Book Description
Software Engineering,Big Data and Intelligent Computing,Computer Science,Deep Learning,Computer Network and Application Technology,Web Information Systems and Applications, Artificial Intelligence and Expert Systems,Database System and Application,Blockchain Technology,Other related theories, Technologies and applications
Author: Mamoun Alazab Publisher: Springer ISBN: 3030130576 Category : Computers Languages : en Pages : 246
Book Description
Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points.
Author: Dong Yu Publisher: Springer ISBN: 1447157796 Category : Technology & Engineering Languages : en Pages : 321
Book Description
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.
Author: IEEE Staff Publisher: ISBN: 9781728197951 Category : Languages : en Pages :
Book Description
IEEE CCNC 2021 will present the latest developments and technical solutions in the areas of home networking, consumer networking, enabling technologies (such as middleware) and novel applications and services The conference will include a peer reviewed program of technical sessions, special sessions, business application sessions, tutorials, and demonstration sessions
Author: Zhenwei Yu Publisher: World Scientific ISBN: 1848164475 Category : Computers Languages : en Pages : 185
Book Description
Introduces the concept of intrusion detection, discusses various approaches for intrusion detection systems (IDS), and presents the architecture and implementation of IDS. This title also includes the performance comparison of various IDS via simulation.
Author: Kwangjo Kim
Author: Kwangjo Kim
Author: Kwangjo Kim
Author: Kwangjo Kim
Author: IEEE Staff
Author: Dhruba Kumar Bhattacharyya
Author: IEEE Staff
Author: Mamoun Alazab
Author: Dong Yu
Author: IEEE Staff
Author: Zhenwei Yu