Topic: Tackling Missing Labels in ML for Security

Next in our spring seminar series, we welcome Dr. Baris Coskun, Principal Scientist at Amazon Wed Services.

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.

Those who cannot attend in-person are welcome to tune in virtually through Webex: https://utsa.webex.com/utsa/j.php?MTID=mab715a7149604891f64ce657baef84e0.