Research

Research Goals:

My long-term goal is to create scalable, automated models for secure system design, provisioning AI-driven services, and the secure management and sharing of quality data within the domain of IoT-engineered platforms. An evolving threat landscape demands developing dynamic security infrastructures, protocols, and solutions that can adapt to user requirements.

Current Research: 

Using Generative Artificial Intelligence to design and develop secure systems and software. 

The expected outcome of this research will align with three research areas of interest. Cyber-Physical Systems (CPS): creating secure-by-design system architectures for CPS platforms (IIoT and ICS) to evaluate the security of systems, their vulnerabilities and mitigation. Applied Cryptography: Evaluate and enhance how cryptography is used on the Internet of Things (IoT) platform. The research of novel designs, and implementation of lightweight low-latency encryptions will be done to enhance and recommend lightweight security protocols. Application of data science, ML, and AI : research will be done on various generative AI models, which will be applied as a part of the solution for the first two research scenarios. It will play a crucial role in the usage of GenAI systems for developing and advancing methods and resources for secure system modeling, which evaluates, identifies, and reduces security threats to CPS. 

Design Secure and Incentive Driven Dataspaces Infrastructure for Enhanced Data Sharing.

The expected outcome of this research will align with the research domains of Formal Methods and Language-based Security (security and privacy preserving compositions), Intelligent Information Integration, and infrastructure for Computational and Data-Enabled Science and Engineering. The applications of the research within these tentative research domains will be addressed through the research, development, and promotion of a data sharing platform called dataspaces.

Secure and Dynamic Service Provisioning for IoT Applications.

Problem: In different IoT based applications like remote healthcare (doctors or nurses remotely monitoring patients) or disaster management (think the forest fires in California or Australia and fire firefighters wanting to remotely monitor the situation and allocate emergency resources) different users may have different service requirements. These requirements can be along the lines of performing a tradeoff between the network's Quality of Service parameters like latency or bandwidth or network security parameters like the strength of encryption standards or authenticating data packets.

Goal: The objective is then to design a system that can perform these tradeoff efficiently addressing the needs of different users, designing network security as a dynamic parameter rather than how it is traditionally designed in a universally static way, and then use machine learning to predict the requests of future users and recommend them to service providers such the energy-constrained IoT devices can be more efficiently managed.

Key-Ideas/Research Domains: Cybersecurity, Machine Learning, service provisioning and recommendations, optimization algorithms, Internet of Things.

Current Students

Ph.D. Students: 

• Damilola Alao

• Oluwafeyisayo Oyeniyi 

• Victorine Clotilde Wakam Younang

MS Students:

• -

Current and Past Undergraduate Students:

• Alec Breslow (P)

• Paul Albrecht (P)

• Giuseppe DiMaio (P)

• Rio Capollari (P)