Money Laundering in the Sharing Economy
The fundamental goal of money laundering is to make criminal income appear as if it has been derived from a legitimate source. Cybercriminals use the sharing economy platforms, including ridesharing, short-term rentals, the gig economy, on-demand delivery, peer-to-peer lending, crowdfunding, reselling and trading, and even entertainment and video games, to launder their ill-gotten gains. The AML research group draws on previous work conducted in criminology and is working toward novel ways to detect criminal activity.
The Impact of Security Alert Overload
To explore the challenges organizations face when running their own SOC or using a virtual SOC provided by a managed security service provider. What makes this research different is the focus on the interplay between organizational, human, and technical challenges present in operating a successful SOC.
Machine Learning Prediction of Transactions, Properties and Anomalies on Cryptocurrency Networks
Predicting anomalies in transaction and other networks will require network theoretic and machine learning approaches that are able to detect interactions and complex relationships between variables in high dimensional data. Our goals are to characterize topological properties of transaction networks, predict future transactions, and identify anomalous behavior. We will develop new network and machine learning approaches to address security and robustness questions for cryptocurrency networks and other time varying networks that may experience unexpected changes. We will combine machine learning and graph neural networks with Kalman filters to predict future transactions and anomalies.
VR Training Simulation Framework
This project aims to create a viable framework for delivering different types of VR training simulations to educators to develop simulations that have multiple correct answers, ordered and unordered series of steps, and valued accuracy of each procedure.
Leveraging Attack Graph State Estimation for Cyber Defense
This project will focus on developing attack graphs showing how a system can be compromised to build and deploy cyber defense tools that continuously monitor the system and adapt to changing conditions.
Trusted AI Through Personalized Explanations PERX & EXPLORE
Building on prior research on trusted human-AI collaboration and transparent decision-making, this project develops two complementary frameworks in the increasingly critical area of Explainable Artificial Intelligence (XAI): Personalized Explanation Systems (Perx) and Explaining Options & Recommendations (EXPLORE). Medical device application provides the data source needed to prototype the framework.
An Interpretable and Trustworthy AI Framework for Smart Grid Cyberattack Detection and Recovery
This project proposes a novel, interpretable, and trustworthy machine learning framework that detects fault and cyber attack incidents associated with the electric power grid and its recovery from critical system incidents in real-time.
Study of dynamics and control of discrete muscle-like actuators
Robots and autonomous systems most often use servomotors to generate the motion required to do their tasks. The control is simple, but a different servomotor (or other actuator) needs to be sized and selected for each and every degree of freedom, which consumes a lot of person-hours of effort. If a back-up system is needed, it is heavy and bulky, and the entire process may need to be repeated again. This cyber-fellows project investigates a different paradigm inspired by human muscles: each degree of freedom is actuated by a collection of modular units that work together the way muscle cells do, with each module being on-off only. In this way only a single part number needs to be tracked and stocked, and adjustments and repairs can be made on the fly. Redundancy can be built-in by including extra modules in the design. However, controlling a system like this is complicated because activation of each module needs to be coordinated with all the others. This project investigates ways to predict how collections of modules will behave in order to plan their structure and activations accordingly.