Michael Galde

Michael Galde is an Assistant Professor at the University of Arizona's College of Engineering, Department of Electrical and Computer Engineering, where he specializes in cybersecurity education and research. He holds a Master's in Cybersecurity and a Bachelor's in Political Science from the University of Nebraska and brings over a decade of experience from defense intelligence, industrial cybersecurity, and academic instruction.

• MS in Cybersecurity, University of Nebraska
• BA in Political Science, University of Nebraska

His research and teaching focus on malware analysis, Industrial Control Systems (ICS) security, reverse engineering, and hands-on cyber operations training. He develops advanced courses and practical tools to strengthen cybersecurity resilience in critical infrastructure. His work bridges technical depth with educational accessibility, empowering the next generation of cyber professionals.

His recent technical projects reflect a strong trend in applied cybersecurity research, especially in OT/ICS monitoring, network visualization, and the integration of AI and NLP into security operations. Projects like GRID-LM, IAES-SOC, and PCAPMap demonstrate innovation in real-time threat detection, large-scale data analysis, and user-friendly tooling for cyber defense.

Notable scientific credentials include:

• Global Industrial Cyber Security Professional (GICSP)
• GIAC Response and Industrial Defense (GRID)

Michael Galde actively contributes to cybersecurity through teaching (80% responsibility), university service (20%), research (2024–present), and external consulting (15%). He mentors students through project-based learning and leads initiatives in developing robust educational and technical frameworks. His research team works on projects including SPINE, DaRIA, and IAES-SOC, focusing on scalable NLP ecosystems and intelligent network monitoring.

He leads multiple research and development efforts, including the IAES-SOC for OT network monitoring, PCAPMap for traffic visualization, and SPINE for NLP infrastructure. These projects are supported by hands-on development and integration with tools like Wazuh, ELK stack, Scapy, and Bokeh.