A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
A high-throughput hardware accelerator for network entropy estimation using sketches
2021
IEEE Access
Network traffic monitoring uses empirical entropy to detect anomalous events such as various types of attacks. However, the exact computation of the entropy in high-speed networks is a difficult process due to the limited memory resources available in the data plane hardware. In this paper, we present a method and hardware accelerator to approximate the empirical entropy of a large data set with high throughput and sublinear memory requirements. Our method uses streaming algorithms that exploit
doi:10.1109/access.2021.3088500
fatcat:txcsvtqe5rhabpnpi7ipjkpf5e