This $600,000 Project Grant award from the National Science Foundation's Division of Computer and Network Systems will support Purdue University's research to enable real-time, high-bandwidth network traffic analysis using machine learning on programmable switches. The project aims to develop novel methods for efficiently mapping popular ML models like decision trees and neural networks onto programmable switch hardware, as well as new switch primitives for computing flow statistics. This will allow network operators to rapidly detect and respond to traffic anomalies, enable performance diagnosis and repair, and improve the overall security and reliability of the internet. The research will also involve training PhD, master's, and undergraduate students, and producing educational materials on programmable switches for networking curricula. This award reflects NSF's statutory mission and was deemed worthy of support based on its intellectual merit and broader impacts.
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