Comprehensive resource exploring deep learning techniques for intrusion detection in various applications such as cyber physical systems and IoT networks 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. It begins by discussing 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). Later chapters cover timely topics including 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. The book presents real-world examples and case studies to highlight practical applications, along with contributions from leading experts who bring rich experience in both theory and practice. Deep Learning for Intrusion Detection includes information on: Types of datasets commonly used in intrusion detection research including network traffic datasets, system call datasets, and simulated datasets The importance of feature extraction and selection in improving the accuracy and efficiency of intrusion detection systems Security challenges associated with cloud computing, including unauthorized access, data loss, and other malicious activities Mobile Adhoc Networks (MANETs) and their significant security concerns due to high mobility and the absence of a centralized authority Deep Learning for Intrusion Detection is an excellent reference on the subject for computer science researchers, practitioners, and students as well as engineers and professionals working in cybersecurity.

Comprehensive resource exploring deep learning techniques for intrusion detection in various applications such as cyber physical systems and IoT networks 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. It begins by discussing 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). Later chapters cover timely topics including 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. The book presents real-world examples and case studies to highlight practical applications, along with contributions from leading experts who bring rich experience in both theory and practice. Deep Learning for Intrusion Detection includes information on: Types of datasets commonly used in intrusion detection research including network traffic datasets, system call datasets, and simulated datasets The importance of feature extraction and selection in improving the accuracy and efficiency of intrusion detection systems Security challenges associated with cloud computing, including unauthorized access, data loss, and other malicious activities Mobile Adhoc Networks (MANETs) and their significant security concerns due to high mobility and the absence of a centralized authority Deep Learning for Intrusion Detection is an excellent reference on the subject for computer science researchers, practitioners, and students as well as engineers and professionals working in cybersecurity.

Comprehensive resource exploring deep learning techniques for intrusion detection in various applications such as cyber physical systems and IoT networks 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. It begins by discussing 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). Later chapters cover timely topics including 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. The book presents real-world examples and case studies to highlight practical applications, along with contributions from leading experts who bring rich experience in both theory and practice. Deep Learning for Intrusion Detection includes information on: Types of datasets commonly used in intrusion detection research including network traffic datasets, system call datasets, and simulated datasets The importance of feature extraction and selection in improving the accuracy and efficiency of intrusion detection systems Security challenges associated with cloud computing, including unauthorized access, data loss, and other malicious activities Mobile Adhoc Networks (MANETs) and their significant security concerns due to high mobility and the absence of a centralized authority Deep Learning for Intrusion Detection is an excellent reference on the subject for computer science researchers, practitioners, and students as well as engineers and professionals working in cybersecurity.