Download or read book Intrusion Detection A Machine Learning Approach written by Jeffrey J P Tsai and published by World Scientific. This book was released on 2011-01-03 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important book introduces the concept of intrusion detection, discusses various approaches for intrusion detection systems (IDS), and presents the architecture and implementation of IDS. It emphasizes on the prediction and learning algorithms for intrusion detection and highlights techniques for intrusion detection of wired computer networks and wireless sensor networks. The performance comparison of various IDS via simulation will also be included.
Download or read book Network Intrusion Detection using Deep Learning written by Kwangjo Kim and published by Springer. This book was released on 2018-10-02 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Intrusion Detection written by Zhenwei Yu and published by World Scientific. This book was released on 2011 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Advanced Computing Technologies and Applications written by Hari Vasudevan and published by Springer Nature. This book was released on 2020-05-06 with total page 686 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features selected papers presented at the 2nd International Conference on Advanced Computing Technologies and Applications, held at SVKM’s Dwarkadas J. Sanghvi College of Engineering, Mumbai, India, from 28 to 29 February 2020. Covering recent advances in next-generation computing, the book focuses on recent developments in intelligent computing, such as linguistic computing, statistical computing, data computing and ambient applications.
Download or read book Intrusion Detection written by Nandita Sengupta and published by Springer Nature. This book was released on 2020-01-24 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state-of-the-art research on intrusion detection using reinforcement learning, fuzzy and rough set theories, and genetic algorithm. Reinforcement learning is employed to incrementally learn the computer network behavior, while rough and fuzzy sets are utilized to handle the uncertainty involved in the detection of traffic anomaly to secure data resources from possible attack. Genetic algorithms make it possible to optimally select the network traffic parameters to reduce the risk of network intrusion. The book is unique in terms of its content, organization, and writing style. Primarily intended for graduate electrical and computer engineering students, it is also useful for doctoral students pursuing research in intrusion detection and practitioners interested in network security and administration. The book covers a wide range of applications, from general computer security to server, network, and cloud security.
Download or read book Network Intrusion Detection using Deep Learning written by Kwangjo Kim and published by Springer. This book was released on 2018-09-25 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Bio Inspired Information and Communications Technologies written by Tadashi Nakano and published by Springer Nature. This book was released on 2021-12-02 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed conference proceedings of the 13th International Conference on Bio-inspired Information and Communications Technologies, held in September 2021. Due to the safety concerns and travel restrictions caused by COVID-19, BICT 2021 took place online in a live stream. BICT 2021 aims to provide a world-leading and multidisciplinary venue for researchers and practitioners in diverse disciplines that seek the understanding of key principles, processes and mechanisms in biological systems and leverage those understandings to develop novel information and communications technologies (ICT). The 20 full and 2 short papers were carefully reviewed and selected from 47 submissions. The papers are organized thematically in tracks as follows: Bio-inspired network systems and applications; Bio-inspired information and communication; mathematical modelling and simulations of biological systems.
Download or read book 2019 3rd International Conference on Electronics Communication and Aerospace Technology ICECA written by IEEE Staff and published by . This book was released on 2019-06-12 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Machine Learning in Intrusion Detection written by Yihua Liao and published by . This book was released on 2005 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Detection of anomalies in data is one of the fundamental machine learning tasks. Anomaly detection provides the core technology for a broad spectrum of security-centric applications. In this dissertation, we examine various aspects of anomaly based intrusion detection in computer security. First, we present a new approach to learn program behavior for intrusion detection. Text categorization techniques are adopted to convert each process to a vector and calculate the similarity between two program activities. Then the k-nearest neighbor classifier is employed to classify program behavior as normal or intrusive. We demonstrate that our approach is able to effectively detect intrusive program behavior while a low false positive rate is achieved. Second, we describe an adaptive anomaly detection framework that is de- signed to handle concept drift and online learning for dynamic, changing environments. Through the use of unsupervised evolving connectionist systems, normal behavior changes are efficiently accommodated while anomalous activities can still be recognized. We demonstrate the performance of our adaptive anomaly detection systems and show that the false positive rate can be significantly reduced.
Download or read book Network Intrusion Detection Using Deep Learning written by Kwangjo Kim and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book NETWORKING 2011 written by Jordi Domingo-Pascual and published by Springer Science & Business Media. This book was released on 2011-04-28 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Computer Networks Big Data and IoT written by A.Pasumpon Pandian and published by Springer Nature. This book was released on 2021-06-21 with total page 980 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Program Modeling written by Garth Stanley Barbour and published by . This book was released on 2002 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Network Anomaly Detection written by Dhruba Kumar Bhattacharyya and published by CRC Press. This book was released on 2013-06-18 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book 2021 International Conference on Emerging Smart Computing and Informatics ESCI written by IEEE Staff and published by . This book was released on 2021-03-05 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This conference aims to present a unified platform for advanced and multi disciplinary research towards design of smart computing and informatics The theme is on a broader front focuses on various innovation paradigms in system knowledge, intelligence and sustainability that may be applied to provide realistic solution to varied problems in society, environment and industries The scope is also extended towards deployment of emerging computational and knowledge transfer approaches, optimizing solutions in varied disciplines of science, technology and healthcare
Download or read book Machine Learning Techniques and Analytics for Cloud Security written by Rajdeep Chakraborty and published by John Wiley & Sons. This book was released on 2021-11-30 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Game Theory and Machine Learning for Cyber Security written by Charles A. Kamhoua and published by John Wiley & Sons. This book was released on 2021-09-08 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
