Join us on June 15th, when m33p will present a talk on advancing anomaly detection systems using more advanced mathematics to determine how to define “normal” traffic, and then find outliers from there. The goal of which is to reduce the level of false positives and increase the detection of anomalies. She will explore three separate methodologies and compare/contrast their individual benefits as anomaly detection systems.
m33p is a soon to be graduate of Seattle Pacific University with 5 years experience in information security. Her interests lie in data analytics, anomaly detection, and statistics.