Intrusion Detection Networks

The rapidly increasing sophistication of cyber intrusions makes them nearly impossible to detect without the use of a collaborative intrusion detection network (IDN). Using overlay networks that allow an intrusion detection system (IDS) to exchange information, IDNs can dramatically improve your overall intrusion detection accuracy.Intrusion Detect

Network Intrusion Detection and Prevention: Concepts and Techniques provides detailed and concise information on different types of attacks, theoretical foundation of attack detection approaches, implementation, data collection, evaluation, and intrusion response. Additionally, it provides an overview of some of the commercially/publicly available intrusion detection and response systems. On the topic of intrusion detection system it is impossible to include everything there is to say on all subjects. However, we have tried to cover the most important and common ones. Network Intrusion Detection and Prevention: Concepts and Techniques is designed for researchers and practitioners in industry. This book is suitable for advanced-level students in computer science as a reference book as well.

This book covers the basic statistical and analytical techniques of computer intrusion detection. It is the first to present a data-centered approach to these problems. It begins with a description of the basics of TCP/IP, followed by chapters dealing with network traffic analysis, network monitoring for intrusion detection, host based intrusion detection, and computer viruses and other malicious code.

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.

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.

Presenting cutting-edge research, Intrusion Detection in Wireless Ad-Hoc Networks explores the security aspects of the basic categories of wireless ad-hoc networks and related application areas. Focusing on intrusion detection systems (IDSs), it explains how to establish security solutions for the range of wireless networks, including mobile ad-hoc networks, hybrid wireless networks, and sensor networks. This edited volume reviews and analyzes state-of-the-art IDSs for various wireless ad-hoc networks. It includes case studies on honesty-based intrusion detection systems, cluster oriented-based intrusion detection systems, and trust-based intrusion detection systems. Addresses architecture and organization issues Examines the different types of routing attacks for WANs Explains how to ensure Quality of Service in secure routing Considers honesty and trust-based IDS solutions Explores emerging trends in WAN security Describes the blackhole attack detection technique Surveying existing trust-based solutions, the book explores the potential of the CORIDS algorithm to provide trust-based solutions for secure mobile applications. Touching on more advanced topics, including security for smart power grids, securing cloud services, and energy-efficient IDSs, this book provides you with the tools to design and build secure next-generation wireless networking environments.

To defend against computer and network attacks, multiple, complementary security devices such as intrusion detection systems (IDSs), and firewalls are widely deployed to monitor networks and hosts. These various IDSs will flag alerts when suspicious events are observed. This book is an edited volume by world class leaders within computer network and information security presented in an easy-to-follow style. It introduces defense alert systems against computer and network attacks. It also covers integrating intrusion alerts within security policy framework for intrusion response, related case studies and much more.

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.