Author : Zhenwei Yu
Publisher : World Scientific
Page : 185 pages
File Size : 31,67 MB
Release : 2011
Category : Computers
ISBN : 1848164475
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
Publisher : Springer
Page : 79 pages
File Size : 27,77 MB
Release : 2018-10-02
Category : Computers
ISBN : 9789811314438
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 :
Page : pages
File Size : 16,57 MB
Release : 2019-06-12
Category :
ISBN : 9781728101682
ICECA 2019 will provide an outstanding international forum for scientists from all over the world to share ideas and achievements in the theory and practice of all areas of aero space technologies Presentations should highlight inventive systems as a concept that combines theoretical research and applications in Electronics, Communication, Information and Aerospace technologies
Author : Dhruba Kumar Bhattacharyya
Publisher : CRC Press
Page : 364 pages
File Size : 38,5 MB
Release : 2013-06-18
Category : Computers
ISBN : 146658209X
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
Author : Jordi Domingo-Pascual
Publisher : Springer Science & Business Media
Page : 492 pages
File Size : 30,91 MB
Release : 2011-04-28
Category : Business & Economics
ISBN : 3642207561
The two-volume set LNCS 6640 and 6641 constitutes the refereed proceedings of the 10th International IFIP TC 6 Networking Conference held in Valencia, Spain, in May 2011. The 64 revised full papers presented were carefully reviewed and selected from a total of 294 submissions. The papers feature innovative research in the areas of applications and services, next generation Internet, wireless and sensor networks, and network science. The first volume includes 36 papers and is organized in topical sections on anomaly detection, content management, DTN and sensor networks, energy efficiency, mobility modeling, network science, network topology configuration, next generation Internet, and path diversity.
Author : A.Pasumpon Pandian
Publisher : Springer Nature
Page : 980 pages
File Size : 22,23 MB
Release : 2021-06-21
Category : Technology & Engineering
ISBN : 9811609659
This book presents best selected research papers presented at the International Conference on Computer Networks, Big Data and IoT (ICCBI 2020), organized by Vaigai College Engineering, Madurai, Tamil Nadu, India, during 15–16 December 2020. The book covers original papers on computer networks, network protocols and wireless networks, data communication technologies and network security. The book is a valuable resource and reference for researchers, instructors, students, scientists, engineers, managers and industry practitioners in those important areas.
Author : Singh, Rajiv
Publisher : IGI Global
Page : 281 pages
File Size : 30,90 MB
Release : 2021-03-18
Category : Technology & Engineering
ISBN : 1799833291
Artificial intelligence has been applied to many areas of science and technology, including the power and energy sector. Renewable energy in particular has experienced the tremendous positive impact of these developments. With the recent evolution of smart energy technologies, engineers and scientists working in this sector need an exhaustive source of current knowledge to effectively cater to the energy needs of citizens of developing countries. Computational Methodologies for Electrical and Electronics Engineers is a collection of innovative research that provides a complete insight and overview of the application of intelligent computational techniques in power and energy. Featuring research on a wide range of topics such as artificial neural networks, smart grids, and soft computing, this book is ideally designed for programmers, engineers, technicians, ecologists, entrepreneurs, researchers, academicians, and students.
Author : Rajdeep Chakraborty
Publisher : John Wiley & Sons
Page : 484 pages
File Size : 50,53 MB
Release : 2021-11-30
Category : Computers
ISBN : 1119764092
MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.
Author : Dong Yu
Publisher : Springer
Page : 329 pages
File Size : 46,33 MB
Release : 2014-11-11
Category : Technology & Engineering
ISBN : 1447157796
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 : Charles A. Kamhoua
Publisher : John Wiley & Sons
Page : 546 pages
File Size : 39,77 MB
Release : 2021-09-08
Category : Technology & Engineering
ISBN : 1119723949
GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.
