Deep Learning for Intrusion Detection

Deep Learning for Intrusion Detection

Provides a practical guide to understand the challenges of intrusion detection in various application areas and how deep learning can be applied to address those challenges.

Basic Concepts

Discusses the basic concepts of intrusion detection systems (IDS) and various deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs).

Advanced Topics

Includes timely topics such as network communication between vehicles and unmanned aerial vehicles. The book closes by discussing security and intrusion issues associated with lightweight IoTs, MQTT networks, and Zero-Day attacks.

Practical Applications

Highlights practical applications with real-world examples and case studies, along with contributions from leading experts with rich experience in both theory and practice.

Datasets and Features

Includes information on types of datasets commonly used in intrusion detection research, such as network traffic datasets, system call datasets, and simulated datasets. Emphasizes the importance of feature extraction and selection in improving accuracy and efficiency.

Security Challenges

Addresses security challenges associated with cloud computing, including unauthorized access, data loss, and other malicious activities. Discusses security concerns in Mobile Adhoc Networks (MANETs) due to high mobility and lack of centralized authority.

Target Audience

Serves as an excellent reference for computer science researchers, practitioners, students, engineers, and professionals working in cybersecurity.