Deep Learning for Intrusion Detection

Faheem Syeed Masoodi and Alwi Bamhdi present Deep Learning for Intrusion Detection, 1st Edition, a reference volume examining neural network architectures applied to network security. The text introduces fundamental principles of intrusion detection systems alongside specific models, including convolutional neural networks, recurrent neural networks, and deep belief networks. It targets computer science researchers, cybersecurity professionals, engineers, practitioners, and students seeking structured coverage of automated threat detection.

The volume addresses security architectures across specialized physical and autonomous environments. Detailed discussions cover intrusion monitoring within cyber-physical systems, Internet of Things (IoT) networks, vehicular communications, and unmanned aerial vehicles. The text further analyzes vulnerabilities unique to lightweight IoT deployments, MQTT messaging networks, and emerging Zero-Day attacks.

To support practical application, the reference incorporates real-world examples and case studies authored by theoretical and practical experts. This structure offers graduate students and security practitioners a grounded guide for evaluating deep learning solutions across complex network infrastructure.