Multi-Fractal Traffic and Anomaly Detection in Computer Communications

This book presents a thorough examination of traffic theory and anomaly detection, making it essential for scholars studying network communications.

The book Multi-Fractal Traffic and Anomaly Detection in Computer Communications offers an in-depth exploration of both mono- and multi-fractal traffic theories. It delves into foundational concepts such as long-range dependent time series and 1/f noise, providing readers with a solid grounding in the principles of ergodicity and predictability that are essential for understanding traffic dynamics. Additionally, it covers traffic modeling and simulation, stationarity tests, and measurement techniques, making it a comprehensive resource for researchers and practitioners alike.

A significant highlight of Multi-Fractal Traffic and Anomaly Detection in Computer Communications is the author's proof that mono-fractal long-range dependent (LRD) time series exhibit ergodicity, thereby establishing that LRD traffic is stationary. The text further examines the stationarity of multi-fractal traffic, emphasizing the importance of observation time scales. To enhance the understanding of these complex phenomena, the author introduces multi-fractional generalized Cauchy processes and modified multi-fractional Gaussian noise, which are pivotal in modern traffic analysis.

In addition to theoretical insights, the book provides practical guidelines for determining the appropriate record length for traffic measurement. It also presents innovative approaches to traffic simulation and addresses the critical issue of anomaly detection, particularly in the context of distributed denial-of-service attacks. This makes Multi-Fractal Traffic and Anomaly Detection in Computer Communications an invaluable resource for scholars and graduate students focused on network traffic within the realm of computer science.