Deep Learning for Intrusion Detection by Faheem Syeed Masoodi (.PDF)
File Size: 9 MB
Deep Learning for Intrusion Detection: Techniques and Applications by Faheem Syeed Masoodi, Alwi Bamhdi
Requirements: .PDF reader, 9 mb
Overview: 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.
Genre: Non-Fiction > Tech & Devices
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