Collaborative Networks for a Sustainable World Aiming to reach a sustainable world calls for a wider collaboration among multiple stakeholders from different origins, as the changes needed for sustainability exceed the capacity and capability of any individual actor. In recent years there has been a growing awareness both in the political sphere and in civil society including the bu- ness sectors, on the importance of sustainability. Therefore, this is an important and timely research issue, not only in terms of systems design but also as an effort to b- row and integrate contributions from different disciplines when designing and/or g- erning those systems. The discipline of collaborative networks especially, which has already emerged in many application sectors, shall play a key role in the implemen- tion of effective sustainability strategies. PRO-VE 2010 focused on sharing knowledge and experiences as well as identi- ing directions for further research and development in this area. The conference - dressed models, infrastructures, support tools, and governance principles developed for collaborative networks, as important resources to support multi-stakeholder s- tainable developments. Furthermore, the challenges of this theme open new research directions for CNs. PRO-VE 2010 held in St.
With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents machine learning techniques in depth to help you more effectively detect and counter network intrusion.
In this book, you'll learn about:
Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.
Team Cooperation in a Network of Multi-Vehicle Unmanned Systems develops a framework for modeling and control of a network of multi-agent unmanned systems in a cooperative manner and with consideration of non-ideal and practical considerations. The main focus of this book is the development of "synthesis-based" algorithms rather than on conventional "analysis-based" approaches to the team cooperation, specifically the team consensus problems. The authors provide a set of modified "design-based" consensus algorithms whose optimality is verified through introduction of performance indices.