Publications
- E. Atwater, C. Bocovich, U. Hengartner, and I. Goldberg. Netsim: Network simulation and hacking for high schoolers. To appear in the 2017 USENIX Workshop on Advances in Security Education (ASE).
- E. Atwater and U. Hengartner. Shatter: Using threshold cryptography to protect single users with multiple devices. In ACM Conference on Security & Privacy in Wireless and Mobile Networks (WiSec), pages 91-102, 2016.
- E. Atwater, C. Bocovich, U. Hengartner, E. Lank, and I. Goldberg. Leading Johnny to Water: Designing for Usability and Trust. In the Symposium On Usable Privacy and Security (SOUPS), pages 69-88, 2015.
- H. Khan, E. A. Atwater and U. Hengartner. A comparative evaluation of implicit authentication schemes. In International Symposium on Research in Attacks, Intrusions and Defenses (RAID), pages 255-275, 2014.
- H. Khan, E. A. Atwater and U. Hengartner. Itus: An implicit authentication framework for Android. In ACM Annual International Conference on Mobile Computing and Networking (MobiCom), pages 507-518, 2014.
- A. Vahdat, E. A. Atwater, A. R. McIntyre, and M. I. Heywood. On the application of GP to streaming data classification tasks with label budgets. In ACM Genetic and Evolutionary Computation Conference (GECCO), pages 1287-1294, 2014.
-
E. A. Atwater.
Towards coevolutionary genetic programming with Pareto archiving under streaming data.
Thesis for Master of Computer Science, Faculty of Computer Science, Dalhousie University, August 2013.
- » Download a linked and bookmarked copy locally
- E. A. Atwater and M. I. Heywood. Benchmarking pareto archiving heuristics in the presence of concept drift: Diversity versus age. In ACM Genetic and Evolutionary Computation Conference (GECCO), pages 885–892, 2013.
- E. A. Atwater, M. I. Heywood, and A. N. Zincir-Heywood. GP under streaming data constraints: A case for Pareto archiving? In ACM Genetic and Evolutionary Computation Conference (GECCO), pages 703–710, 2012.
- E. A. Atwater. A preliminary investigation of different feature sets for skype traffic classification using machine learning. Thesis for Bachelor of Computer Science with Honours, Faculty of Computer Science, Dalhousie University, April 2011.