Bio
He obtained a Computer Science Ph.D. degree at the Luddy School of Informatics, Computing, and Engineering on March 27th, 2022. His research focused on reverse-engineering the security of IoT devices and building an intelligent dynamic analysis tool on top of existing static/dynamic analysis security tools. His Ph.D. thesis was based on developing a phishing prevention solution by fingerprinting visiting websites with machine learning processes of the collected features of the IoT network traffic.
Recent Publications
Keywords: IoT, MUD, MUD-Visualizer. [bibtex-entry]
Keywords: IoT, MUD, MUD-Visualizer. [bibtex-entry]
Keywords: IoT, MUD, MUD-Visualizer. [bibtex-entry]