Tackling Missing Labels in ML for Security

Baris Coskun, Ph.D. 

Principal Scientist at Amazon Wed Services

2/24/2023

11AM – 12PM CST

WebEx: https://utsa.webex.com/utsa/j.php?MTID=mab715a7149604891f64ce657baef84e0

Abstract:  Lack of reliable labelled data remains to be one of the major challenges for applying ML to cybersecurity problems. In this talk, Baris will present how recent unsupervised methods can be adopted to alleviate part of the problem. He will discuss how autoencoder architectures can be used to identify suspicious activities in compute environments. He will also talk about how these ML models can be evaluated in the absence of ground truth data using weak signals.