CAREER: A General Framework for Methodical and Interpretable Anomaly Mining

 
PI: Leman Akoglu Phone: 1 (631) 632 9801
Department of Computer Science Fax:     1 (631) 632 1784
Stony Brook University Email: leman AT cs.stonybrook.edu
Stony Brook, NY 11794 Website: http://www.cs.stonybrook.edu/~leman

This material is based upon work supported by the National Science Foundation under Grant No. 1452425. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

1. GENERAL INFORMATION

1.1. Abstract

Link to NSF abstract

1.2. Keywords

Anomaly Mining, Novel Anomaly Definitions, Descriptive Methods, Anomaly Ensembles, Anomaly Mining Framework, Heterogeneous Data Mining,
Graph Mining, Applications.

1.3. Funding agency


2. PEOPLE INVOLVED

In addition to the PIs, the following graduate students work on the project.

3. RESEARCH

3.1. Project goals

Technical Merits:

This proposal aims to push the boundaries of anomaly mining as a field through a quest for principled foundations and practices. Research will create previously unstudied classes of data representations that unify heterogeneous data sources, and build on them to formulate novel anomaly mining problems. We will invent new, descriptive algorithms for complex anomaly detection and characterization, that will also explore and exploit ensemble and multi-view approaches. The proposed research will give rise to a comprehensive framework for anomaly mining; through a deeper understanding of the space of problems and objectives, new models and algorithms, and systematic techniques to harness them.

Broader Impacts:

Societal: Proposed research will take the essential steps to mature anomaly mining into a valuable contributor to the larger world. It will have direct significance to many concrete problems (e.g., outsider threat, fraud, intrusion) important for the government, industry, and the society. The project will build a web-based platform that hosts a repository of formulations, algorithms, tools, and datasets, for the research community and the public to leverage. We will collaborate with industry and hospital partners to shepherd our innovations into deployed technology, with tangible impact on security and healthcare.
Educational: The PI is committed to developing an education plan that: trains students to think creatively in formulating and solving problems, enhances undergraduate training by involving Honors thesis students in proposed research, promotes campus wide synergism for students across departments, and increases the role of women in Computer Science through mentoring and open house events for women in the community.

The project summary can be found here.

3.2. Results

Our main results so far are as follows:
  • Under construction

3.3. Related Publications

3.4. Related Workshops





4. EDUCATION

The educational contributions of the project include:

Point of Contact: Leman Akoglu, leman AT cs.stonybrook.edu


Last updated: June 2016, by Leman Akoglu