The Extended Abstract should be up to 2 pages long ( a page 3rd only for references). All abstracts must include the following sections:
Introduction section,
Problem and Motivation: This section should clearly state the problem being addressed and explain the reasons for seeking a solution to this problem.
Background and Related Work: This section should describe the specialized (but pertinent) background necessary to appreciate the work. Include references to the literature where appropriate, and briefly explain where your work departs from that done by others. Reference lists do not count towards the limit on the length of the abstract.
a methodology section,
This section should describe your approach in investigating the problem and should clearly state how your approach is novel.
a results (or partial results) section,
This section should clearly show how the results of your work contribute to computer science and should explain the significance of those results.
and a conclusion section.
The Extended Abstract must follow the IEEE Conference Template found here in Overleaf.
The submission must be blinded. (I.e., remove the authors or anything that can identify the authors)
Students who submit must be available on December 5th to present their work.
The Accepted Extended Abstracts will be published in the Conference Program.
Click the botton below to Submit your Extended Abstract. Please remember to blind your submission (remove the authors)
The presentation duration will be 15 minutes long with 3 minutes for questions. (This might change).
Posters must be organized in landscape format, 48" width x 36" height. The posters will be presented in the form of Lighting talks of 5 minutes. The presenters will stay until the end of the talks for questions from the audience.
Towards a framework for Network-based Malware Detection System
Automated Anomaly Detection Within The Toa Network Flow Data Monitoring System
Modules to teach parallel computing using Python and the LittleFe Cluster
HYPERSPECTRAL AND MULTISPECTRAL IMAGE FUSION USING A MULTI-LEVEL PROPAGATION LEARNING NETWORK (Full paper in IEEE Conference format)
Mobile Malware Detection using Multiple Detector Set Artificial Immune System