Intrusion Detection A Machine Learning Approach PDF Download
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Author | : Jeffrey J P Tsai |
Publisher | : World Scientific |
Total Pages | : 185 |
Release | : 2011-01-03 |
Genre | : Computers |
ISBN | : 1908978260 |
Download Intrusion Detection: A Machine Learning Approach Book in PDF, ePub and Kindle
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.
Author | : Kwangjo Kim |
Publisher | : Springer |
Total Pages | : 79 |
Release | : 2018-10-02 |
Genre | : Computers |
ISBN | : 9789811314438 |
Download Network Intrusion Detection using Deep Learning Book in PDF, ePub and Kindle
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 | : Zhenwei Yu |
Publisher | : World Scientific |
Total Pages | : 185 |
Release | : 2011 |
Genre | : Computers |
ISBN | : 1848164475 |
Download Intrusion Detection Book in PDF, ePub and Kindle
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 | : Hari Vasudevan |
Publisher | : Springer Nature |
Total Pages | : 686 |
Release | : 2020-05-06 |
Genre | : Technology & Engineering |
ISBN | : 9811532427 |
Download Advanced Computing Technologies and Applications Book in PDF, ePub and Kindle
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.
Author | : Nandita Sengupta |
Publisher | : Springer Nature |
Total Pages | : 151 |
Release | : 2020-01-24 |
Genre | : Computers |
ISBN | : 9811527164 |
Download Intrusion Detection Book in PDF, ePub and Kindle
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.
Author | : IEEE Staff |
Publisher | : |
Total Pages | : |
Release | : 2019-06-12 |
Genre | : |
ISBN | : 9781728101682 |
Download 2019 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA) Book in PDF, ePub and Kindle
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 | : Kwangjo Kim |
Publisher | : Springer |
Total Pages | : 79 |
Release | : 2018-09-25 |
Genre | : Computers |
ISBN | : 9811314446 |
Download Network Intrusion Detection using Deep Learning Book in PDF, ePub and Kindle
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 | : Tadashi Nakano |
Publisher | : Springer Nature |
Total Pages | : 276 |
Release | : 2021-12-02 |
Genre | : Science |
ISBN | : 3030921638 |
Download Bio-Inspired Information and Communications Technologies Book in PDF, ePub and Kindle
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.
Author | : Yihua Liao |
Publisher | : |
Total Pages | : 230 |
Release | : 2005 |
Genre | : |
ISBN | : |
Download Machine Learning in Intrusion Detection Book in PDF, ePub and Kindle
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.
Author | : A.Pasumpon Pandian |
Publisher | : Springer Nature |
Total Pages | : 980 |
Release | : 2021-06-21 |
Genre | : Technology & Engineering |
ISBN | : 9811609659 |
Download Computer Networks, Big Data and IoT Book in PDF, ePub and Kindle
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.