CyVIA revolutionizes risk assessment by automating the entire workflow, from data collection to analysis, eliminating the need for manual evaluation. Unlike traditional frameworks, it enables continuous risk monitoring and adaptive threat analytics. Key benefits include identifying network and service dependencies, assessing infrastructure risks, detecting vulnerabilities in operating systems and applications, and classifying weaknesses based on severity and access vectors. CyVIA also highlights the most vulnerable products, prioritizes critical threats for remediation, and provides relational analyses of vulnerabilities, products, and weakness types while monitoring anomalous user activities based on emerging threats.
Repository URL
We are working on simulating a large network with active traffic to understand and flag malicious applications and activities.
Using artificial intelligence and machine learning algorithms to develop risk assessment framework.
Faculty:
Dr. Nazia Sharmin, Appalachian State University
Faculty:
Dr. Deepak Tosh, Associate Professor, University of Texas at El Paso
Dr. Martin Zhao, Associate Professor, Mercer University
Students:
Hayden Amazon, Cybersecurity Major Undergraduate
Research Poster @ BEAR Day 2024
Reilly Holbel, Cybersecurity Major, Undergraduate