Research
Doctoral dissertation
Oakland University · expected 2027
Control-dynamics-aware safety–security co-engineering for connected and autonomous vehicles. The thesis is that safety and security of a vehicle cannot be assessed independently once the attacker can reach the control loop: a stealthy manipulation of a sensor or a message changes the plant's dynamics, and the right place to analyze it is the control model itself. The framework ties threat analysis to the vehicle's dynamic model so that both the hazard and the attack path are derived from the same artifacts.
Publications and preprints
eIT 2026 · IEEE TechRxiv
Stealthy attacks on connected autonomous vehicles: GPS spoofing and integrator windup in vehicle control; detection and mitigation from the controller's point of view. Further papers in preparation on the co-engineering framework and on formal-concept-analysis-based mapping between hazards and threats.
Teaching
Trine University · Ivy Tech · Baker College · since 2011
Graduate courses in model-based systems engineering, systems verification and validation, and software-defined networking (Trine); Linux and cloud virtualization (Ivy Tech); electrical engineering, IT and penetration testing (Baker, 2011 – 2019). Course development including SysML v2 exercises and interactive content.
Methods and technologies
What the research actually uses, from the theory to the bench:
Control and systems theory
State-space models of vehicle longitudinal and lateral dynamics, observers and residual-based anomaly detection, integrator anti-windup, stability and reachability arguments under adversarial inputs.
Threat and hazard analysis
ISO/SAE 21434 TARA, ISO 26262 HARA, STPA and STPA-Sec, attack trees and attack-path analysis, MITRE ATT&CK mapping.
Formal Concept Analysis
Lattice-based mapping between hazards, threats, assets and controls, used to find co-dependencies that tabular methods miss.
Adversarial machine learning
Robustness of perception and decision models to adversarial inputs; evaluation methodology for safety-relevant ML.
Model-based engineering and test
MATLAB/Simulink and Stateflow, SysML v2, HIL and SIL test generation driven by threat scenarios, Vector CANoe/CANalyzer, Python tooling.
Vehicle networks and security mechanisms
CAN/CAN-FD and automotive Ethernet, SecOC, MACsec, HSM-based key management, secure boot, GNSS and V2X attack surfaces.
Education
PhD Candidate, ECE
Oakland University · GPA 3.88 · expected 2027
M.S. Information Assurance
Davenport University · GPA 3.76
B.S. Electronics Engineering
ITVER, Veracruz, Mexico · digital systems
Research areas
Safety–security co-engineeringcore
Control theory and CAV dynamicscore
Adversarial ML robustnessactive
Formal Concept Analysisnovel
MBSE / SysML v2applied
Advisor
Dr. Subra Ganesan
Oakland University · ECE