This three-year, $244,997 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop scalable methods for analyzing large-scale network traffic data to quantify internet-of-things (IoT) device insecurities and characterize malicious IoT campaigns.
The awardee, San Diego State University Research Foundation, will work with collaborators to design algorithms and formal methods using supervised deep learning to fingerprint exploited IoT devices on an internet scale. Researchers will also develop IoT-specific feature engineering and clustering algorithms, execute malware analysis, and engineer computational approaches on packet sequences to investigate IoT scanning and deception techniques.
The project seeks to establish unique, empirically-derived datasets on actively and passively collected network traffic and service banners related to malicious IoT activity. Findings will support proactive IoT security remediation, research and training through a public cyberinfrastructure indexing compromised IoT devices and associated threat information. Educational activities and workshops are also planned to engage underrepresented groups.
Generated 1/6/24, 11:39 AM