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2016 Symposia

A Ubiquity symposium is an organized debate around a proposition or point of view. It is a means to explore a complex issue from multiple perspectives. An early example of a symposium on teaching computer science appeared in Communications of the ACM (December 1989).

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Ubiquity Symposium: The Internet of Things

Table of Contents

  1. The Third Wave (Opening Statement) by Kemal Delic
  2. Discovery in the Internet of Things by Arkady Zaslavsky and Prem Prakash Jayaraman
  3. W3C Plans for Developing Standards for Open Markets of Services for the IoT  by Dave Raggett
  4. Standards for Tomorrow by Dejan Milojicic, Paul Nikolich, and Barry Leiba
  5. A Case for Interoperable IoT Sensor Data and Meta-data Formats by Milan Milenkovic
  6. Programmable IoT: On The role of APIs in IoT by Maja Vukovic
  7. Fog Computing Distributing Data and Intelligence for Resiliency and Scale Necessary for IoT by Charles Byers and Patrick Wetterwald
  8. Evolution and Disruption in Network Processing for The Internet of Things by Lorenzo di Gregorio
  9. The Importance of Cross-Layer Considerations in a Standardized WSN Protocol Stack Aiming for IoT by Bogdan Pavkovic, Marko Batic, and Nikola Tomasevic
  10. Using Redundancy to Detect Security Anomalies Toward IoT Security Attack Detectors by Mladen A. Vouk and Roopak Venkatakrishnan
  11. Ensuring Trust and Security in the Industrial IoT by Bernardo A. Huberman
  12. On Resilience of IoT Systems by Kemal Delic
  13. IoT in Energy Efficiency by Francois Jammes
  14. IoT: Promises, Perils, Perspectives (Closing Statement) by Kemal Delic

Symposia

2018
  • When Good Machine Learning Leads to Bad Security: Big Data (Ubiquity symposium)

    While machine learning has proven to be promising in several application domains, our understanding of its behavior and limitations is still in its nascent stages. One such domain is that of cybersecurity, where machine learning models are replacing traditional rule based systems, owing to their ability to generalize and deal with large scale attacks which are not seen before. However, the naive transfer of machine learning principles to the domain of security needs to be taken with caution. Machine learning was not designed with security in mind and as such is prone to adversarial manipulation and reverse engineering. While most data based learning models rely on a static assumption of the world, the security landscape is one that is especially dynamic, with an ongoing never ending arms race between the system designer and the attackers. Any solution designed for such a domain needs to take into account an active adversary and needs to evolve over time, in the face of emerging threats. We term this as the "Dynamic Adversarial Mining" problem, and this paper provides motivation and foundation for this new interdisciplinary area of research, at the crossroads of machine learning, cybersecurity, and streaming data mining.