Our research focuses on the design, development, experimentation and operationalization of computationally intelligent, large-scale, and distributed decision-support systems for autonomous cyber operations. To build, test and deploy autonomous cyber-systems that enable intelligent cybersecurity data analytics, our research team explores the science of information, computation, learning, and fusion as it pertains to the use of artificial intelligence for adaptive pattern discovery, learning, reasoning, perception, action and decision-making given heterogenous, disparate cyber data.
Our research aims to provide the Army Cyber Enterprise new capabilities to:
- Shift emphasis from sensing to information awareness
- Understand the underpinning of cyber autonomy
- Relieve human cybersecurity analyst cognitive overload in dealing with the data deluge problem
- Enhance human-machine interface in information processing
Our research has the potential to:
- Cope with various complex disparate data/information types
- Integrate a diversity of unique reasoning and learning components collaborating simultaneously
- Bridge correlational with causal discovery
- Determine solutions or obstructions to local-to-global data fusion problems
- Mechanize reasoning/learning and computing in the same computational environment
- Yield provably efficient procedures to enable or facilitate cyber data analytics
- Deal with high-dimensional and massive cyber datasets with provably guaranteed performance
Our current research efforts include:
- "Adversarial Machine Learning in the Network Intrusion Detection Setting"
- "Intelligent Feature Engineering for High-Performance Computing Enabled Machine Learning in Cybersecurity"
Our previous research efforts include:
- "A Machine Learning Approach to Robust Malware Classification under Adversarial Conditions"
- "The Cyber Force Manning Model: Optimizing Army Cyber Branch Readiness and Manning Under Uncertainty"