I am a fifth year graduate student at the University of California, Davis. I am advised by Prof. Karl N. Levitt. Besides Professor Levitt, I also work closely with Professors Micheal Gertz and Felix Wu. I am a member of the Security lab of the University of California, Davis one of the NSA's Centers of Academic Excellence in Information Assurance Education . My primary research interest is in Intrusion detection and applied machine learning in computer security.
Research
My primary research focus is on the detection and management of Phishing emails using text analysis and classification techniques. Below are some of the projects I am working on:- Authorship Identification Forensics: We study unsupervised learning techniques to identify authorhip of Phishing emails based on email structures and linguistic patterns found in Phishing emails.
- Kernel Feature Extraction: We study kernel methods; Kernel Principal Component Analysis (KPCA); Kernel Linear Discriminant Analysis (KLDA) and Kernel Maximum Margin Discriminant Analysis (KKMDA) to perform online feature extraction on a Phishing repository.
- Diversity Algorithm for Worrisome Software and Networks (DAWSON): We study how is to break the vulnerability specification for the executing component code or protocol that an attacker is exploiting without breaking the functionality of the executing component or protocol. A high level abstraction of defense-in-depth.
Publications
- Ebrima N. Ceesay, Michael Gertz, Omar Alonso: Authorship Identification Forensics
on Phishing Data. International Conference on Data Engineering
(ICDE), Istanbul, Turkey, 2007.
- Ebrima N. Ceesay, Jingmin Zhou , Michael Gertz, Karl N. Levitt, Matt Bishop.
UsingType Qualifiers to Analyze Untrusted Integers and Detecting Security Flaws in C Programs.
Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA) 2006.
- Ebrima N. Ceesay, Melissa Danforth, Todd Heberlein, Karl N. Levitt: Scalable Security Analysis of Networks with Attack Graphs using Expert System. 21st Annual Computer Security Applications Conference (ACSAC), 2005.
Technical Reports & Other Works
Resourceful Links
C/C++ Programming
Machine Learning Softwares
Machine Learning Journals
Machine Learning Benchmark Datasets
Useful Courses and Links in Machine Learning
Related Conferences in Machine Learning
