Bahari Belaton

Data and Information Visualization Main research interest is in the area of scientific data visualization. Initially our focus was in looking at ways of controlling mapping artifacts arising from various ad-hoc and ill-defined assumptions in existing data visualization tools. One possible solution is to treat data modeling function (interpolation and approximation) in the mapping techniques as a separate process, independent of the mapping process itself. Toward this end, an alternative model named as Model Centered Approach (MCA) has been proposed. Further research on MCA has extended it usage into internet-based visualization system. In this case, the compact representation of the modeling function has made it more useful for internet-based visualization – the model that will generate the data is send to the peer hosts, rather than the data themselves which are often huge in size. Recent work concentrates on the development of algorithm to optimize 3D surface(s) reconstruction from a series contour’s slices. In this work we optimizes the reconstruction time while preserving the output at an acceptable level i.e. no clear surface artifacts likes holes. Good balances between reconstruction time and surface quality for a series of dataset have been reported by a Master thesis completed recently. We are now looking for a more thorough study on the slice reduction method, at the same time research on the reconstruction of surfaces from sparse contour slices is high in our research agenda. Current work in this area is focusing on the extraction of feature lines known as crest-lines from 3D medical dataset. The aim is to use the extracted feature lines in detecting important landmarks on a craniofacial dataset. Automating this landmarks identification and placement is crucial to reduce the tedious and error-prone manual inspection. Feature lines based on crest points (also known as extremal points) are invariant to common transformation, and thus they are good for features registration and matching between objects. Research Interests : Network Security Primary focus is on improving existing IDS/IPS solutions by enriching and enhancing alerts they produced with verifiable and quantifiable data quality metrics. Initial work in this area was the establishment of a model called Intrusion Alert Quality Framework (IAQF). It main purpose is to assist network and security engineers in making effective and efficient decisions about the security status of ICT assets under their control. With IAQF we have enriched the alerts generated by IDS/IPS with data quality metrics (alive-correctness, os-correctness, service-correctness, etc) and quantify them in term of score. These quality metrics are verified using contextual information (host’s profiles – os, patch level, application version, etc) from the network environment being monitored in an iterative fashion following a typical cycle of TDQM process. The verified and enriched alerts are finally encoded in the standard format (Intrusion Detection Message Exchange Format or IDMEF) widely used by IDS/IPS communities.