Machine Learning for Embedded System Security

This book comprehensively covers the state-of-the-art security applications of machine learning techniques.  The first part explains the emerging solutions for anti-tamper design, IC Counterfeits detection and hardware Trojan identification. It also explains the latest development of deep-learning-based modeling attacks on physically unclonable functions and outlines the design principles of more resilient PUF architectures. The second discusses the use of machine learning to mitigate the risks of security attacks on cyber-physical systems, with a particular focus on power plants. The third part provides an in-depth insight into the principles of malware analysis in embedded systems and describes how the usage of supervised learning techniques provides an effective approach to tackle software vulnerabilities.