Current Research:
I'm currently working with Anthony Joseph to use statistical machine learning techniques in security sensitive environments that could benefit from adaptive automated techniques; in particular, the crux of this research focuses on identifying virus email traffic. The following are my current research interests:
Security in machine learning:
In this project, we are studying the effect a malicious user can have on statistical learning techniques used in security sensitive environments.
- Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Nina Taft and J. D. Tygar, Compromising PCA-based Anomaly Detectors for Network-Wide Traffic, EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2008-73, 2008.[pdf | bibtex]
- Marco Barreno, Blaine Nelson, Anthony D. Joseph, and J. D. Tygar, The Security of Machine Learning, EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2008-43, 2008. pdf
- Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I.P. Rubinstein, Udam Saini, Charles Sutton, J. D. Tygar, and Kai Xia, Exploiting Machine Learning to Subvert Your Spam Filter, In the Proceedings of the First USENIX Workshop on Large-Scale Exploits and Emergent Threats (LEET'08), San Francisco, CA, April 15, 2008. pdf
- Blaine Nelson, and Anthony D. Joseph, Bounding an Attack's Complexity for a Simple Learning Model , In the Proceedings of the First Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SysML) , Saint-Malo, France, June, 2006. pdf
- Marco Barreno, Blaine Nelson, Russell Sears, Anthony D. Joseph, and J. D. Tygar, Can Machine Learning Be Secure? (Invited paper) , In the Proceedings of the ACM Symposium on InformAtion, Computer and Communications Security (ASIACCS'06) , Taipei, Taiwan, March, 2006. [pdf | bibtex]
SAT-based DTP
This project has focused on developing solvers for large instances of Disjunctive Temporal Problems (DTPs) by converting them into a SAT representation.
- Blaine Nelson and T. K. Satish Kumar, CircuitTSAT: A Solver for Large Instances of the Disjunctive Temporal ProblemTo appear in the Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), 2008. PDF not yet available
Clustering with Pairwise Constraints
This project explored the use of pairwise constraints between data points for clustering algorithms. The constraints we considered indicated whether pairs of points belonged to the same cluster or to different clusters. Using these constraints, one is able to better cluster data as has been demonstrated in several image applications. Our contribution was a new sampling algorithm that uses these constraints.
- Blaine Nelson and Ira Cohen, Revisiting Probabilistic Models for Clustering with Constraints, In the Proceedings of the International Conference on Machine Learning (ICML), 2007. pdf
Adaptive Protection Environment
This project uses machine learning techniques to identify viruses in email traffic.
- Marco Barreno, Blaine Nelson, Russell Sears, and Anthony D. Joseph, User Model Transfer for Email Virus Detection, In the Proceedings of the First Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SysML), Saint-Malo, France, June, 2006. pdf
- Steven Martin, Anil Sewani, Blaine Nelson, Karl Chen, and Anthony D. Joseph, Analyzing Behavioral Features for Email Classification, In the Proceedings of the IEEE Second Conference on Email and Anti-Spam (CEAS 2005), July, 2005. pdf