COREII is a modular cybersecurity research platform that integrates AI, ML, large language models, and advanced analytics to collect, analyze, and correlate data. Running on Kubernetes with open-source tools, it enables flexible deployment, knowledge graphs, and RAG-enhanced data lakes.

COREII Scout helps analysts collect and analyze cyber attack data by automating report writing. Using NLP for entity extraction, ACE for classification, and an LLM for report generation, it streamlines workflows, improves accuracy, and supports efficient threat analysis.

This code processes Zeek logs from network packet captures to identify anomalies in traffic patterns, potentially signaling malicious activity. It uses DBSCAN clustering with Gower distance for feature extraction and a transformer model to assign anomaly scores, with configurable thresholds for detection.

The purpose of this code is to use Machine Learning to spot unusual patterns in network traffic, which could indicate malicious activity. It does this by applying a Graph Neural Network that builds a graph-based view of the network logs and detects anomalies within the local graph context.

This code detects Living-Off-the-Land attacks by analyzing Zeek logs from network traffic. After preprocessing, it uses K-Means to label devices, applies a Graph Convolutional Network to generate embeddings, and then clusters these embeddings to identify suspicious patterns.

The HSA is a statistics-based anomaly detection model that uses graph theory to analyze data relationships across time and scale. It works on diverse data types—such as cybersecurity, materials testing, and GIS—providing unsupervised detection of unusual patterns while reducing false positives with a multi-filter process.

The code fetches an image, places a button behind the image that detects user touch via Unity cues, converts 3D input to pixel space and estimates the corresponding pixel position.

The Verifiable Fuel Cycle Simulation (VISION) is a system-dynamics model of the entire nuclear fuel cycle, from mining to disposal, using user-defined deployment scenarios, fuel recipes, and recycling parameters. It is written in the PowerSim simulation environment and utilized MS Excel to manage data inputs and outputs

This software allows the user to visualize Wireshark or other network capture logs in a waterfall graphic display that the user can quickly find data that looks unique or atypical, such as human entered commands over the network in a non-repetitive manner.

This project was designed to take Open Charge Point Protocol (OCPP) 2.0.1 log data and calculate the aggregate Interim KPIs for the given data.