Three Ph.D. students from the ebiquity lab have posters at the ACM Student Research Competition and General Poster Session of the 2012 Grace Hopper Celebration of Women in Computing conference. The GHC conference is the largest technical conference for women in computing and results in collaborative proposals, networking and mentoring for junior women and increased visibility for the contributions of women in computing. Conference presenters are leaders in their respective fields, representing industry, academia and government. Top researchers present their work while special sessions focus on the role of women in today’s technology fields.
The three ebiquity lab students with posters this year are:
Automation of Cloud Services lifecycle by using Semantic technologies,
Karuna Panda Joshi
We have developed a new framework for automating the configuration, negotiation and procurement of services in a cloud computing environment using semantic web technologies.We have developed detailed Ontologies for the framework. We have designed a prototype, called Smart Cloud Services, which is based on this framework and also incorporates NIST’s policies on cloud computing. This prototype is integrated with different cloud platforms like Eucalyptus and VCL.
A Knowledge-Based Approach To Intrusion Detection Modeling,
M. Lisa Mathews
Current state of the art intrusion detection and prevention systems (IDPS) are signature-based systems that detect threats and vulnerabilities by cross-referencing the threat/vulnerability signatures in their databases. These systems are incapable of taking advantage of heterogeneous data sources for analysis of system activities for threat detection. This work presents a situation-aware intrusion detection model that integrates these heterogeneous data sources and builds a semantically rich knowledge-base to detect cyber threats/vulnerabilities.
Unsupervised Coreference Resolution for FOAF Instances,
Jennifer Alexander Sleeman
Coreference Resolution determines when two entity descriptions represent the same real world entity. Friend of a Friend (FOAF) is an ontology about people and their social networks. Currently there is not a way to easily recognize when two FOAF instances represent the same entity. Existing techniques that use supervised learning typically do not support incremental processing. I present an unsupervised approach that supports both heterogeneous data and incremental online processing.
