Steganography is the art of communicating a secret message, hiding the very existence of a secret message. This book is an introduction to steganalysis as part of the wider trend of multimedia forensics, as well as a practical tutorial on machine learning in this context.
An interdisciplinary framework for learning methodologies, covering statistics, neural networks, and fuzzy logic, Learning from Data provides a unified treatment of the principles and methods for learning dependencies from data.
Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, and defense, to name a few.
The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available.
This text presents significant and historic papers on evolutionary computation ranging from the 1950s to the early 1990s. Introductions to each paper provide... Læs mere
This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of... Læs mere
The book covers several of the major data analysis techniques used to analyze data from high-throughput molecular biology and genomics experiments. It also explains the major concepts behind most of the popular techniques and examines some of the simpler techniques in detail.
This book introduces reinforcement learning, and provides novel ideas and use cases to demonstrate the benefits of using reinforcement learning for Cyber Physical Systems. Two important case studies on applying reinforcement learning to cybersecurity problems are included.
This comprehensive book offers valuable insights while using a wealth of examples and illustrations to effectively demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security.