Search results

Modern society depends critically on computers that control and manage the systems on which we depend in many aspects of our daily lives. While this provides conveniences of a level unimaginable just a few years ago, it also leaves us vulnerable to attacks on the computers managing these systems. In recent times the explosion in cyber attacks, including viruses, worms, and intrusions, has turned this vulnerability into a clear and visible threat. Due to the escalating number and increased sophistication of cyber attacks, it has become important to develop a broad range of techniques, which can ensure that the information infrastructure continues to operate smoothly, even in the presence of dire and continuous threats. This book brings together the latest techniques for managing cyber threats, developed by some of the world's leading experts in the area. The book includes broad surveys on a number of topics, as well as specific techniques. It provides an excellent reference point for researchers and practitioners in the government, academic, and industrial communities who want to understand the issues and challenges in this area of growing worldwide importance.

There is a growing body of literature that focuses on the similarities and differences between how people behave in the offline world vs. how they behave in these virtual environments. Data mining has aided in discovering interesting insights with respect to how people behave in these virtual environments. The book addresses prediction, mining and analysis of offline characteristics and behaviors from online data and vice versa. Each chapter will focus on a different aspect of virtual worlds to real world prediction e.g., demographics, personality, location, etc.

AbstractWidespread public crises often give rise to the proliferation of sensationalized rumors and conspiracy theories, which can evoke a variety of public emotions. Despite the growing importance of research on the relationship between emotions and coping behaviors in crisis, a dearth of natural observation-based investigation has been limiting theory development. To address this gap, this study conducted computational research to study the U.S. public's discrete emotions and coping behaviors during the COVID-19 outbreak crisis, analyzing Twitter data, Google Trends data, and Google Community Mobility data. The results revealed that anger and fear were relatively more prominent emotions experienced by the public than other discrete emotions. Regarding the impacts of emotions on coping behaviors, it was found that the prevalence of low-certainty and retreat emotions was related to increased information-seeking and information-transmitting behaviors. Also, the prevalence of both high-certainty and low-certainty emotions during the COVID-19 outbreak was positively related to the public's compliance with public health recommendations.