Research
Our research examines the security of computer systems across embedded, virtualized, and networked environments. We develop practical approaches to memory protection, trusted execution, automated analysis, and runtime threat detection through hardware and software design.
View all publicationsMemory Protection
- Memory safety
- Kernel isolation
- Access control
Memory errors and excessive access privileges can let faults spread across system components. We develop fine-grained memory-protection and isolation mechanisms for applications, kernel extensions, and resource-constrained devices.
Selected publications
Trusted Execution
- Trusted execution
- Secure virtualization
- Data protection
Sensitive computation depends on well-defined protection boundaries across the software stack. We study hardware-backed isolation, trusted execution, and virtualization mechanisms that protect code and data while keeping isolation overhead practical.
Selected publications
Automated Analysis
- Program analysis
- Fuzzing
- Protocol analysis
Understanding software behavior and interfaces is essential for security analysis and testing, but often requires substantial manual effort. We develop automated techniques for binary analysis and function-level fuzzing, and study how field structure inferred from communication traces can support protocol and traffic analysis.
Selected publications
Runtime Monitoring
- Runtime monitoring
- Intrusion detection
- Hardware-assisted analysis
Detecting attacks requires useful observations of system state and execution. We study efficient runtime-information collection, kernel-integrity monitoring, and hardware-assisted behavior analysis to support learning-based intrusion and anomaly detection.
Selected publications
- SBGen: A Framework to Efficiently Supply Runtime Information for a Learning-based HIDS for Multiple Virtual Machines
IEEE Access 2020
- DADE: a fast data anomaly detection engine for kernel integrity monitoring
The Journal of Supercomputing 2019
- Real-Time Anomalous Branch Behavior Inference with a GPU-inspired Engine for Machine Learning Models
DATE 2019