Nandi Leslie - Raytheon
Students: Spring 2025, unless noted otherwise, sessions will be virtual on Zoom.
Using Machine Learning for Network Intrusion Detection
Jun 24, 2020
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Abstract
Using semi-supervised learning, I propose an anomaly-based network intrusion detection system (NIDS) to detect and classify anomalous and/or malicious traffic. With this proposed machine learning approach, we detect botnet traffic and distinguish it from the normal and background traffic in the IPv4 flow datasets. I evaluate the prediction performance results for the flow-based NIDS algorithms. I show an improvement in detection accuracy and reduction in error rates, when compared with signature-based NIDS and previous studies.About the Speaker
Before joining Raytheon, Dr. Leslie led and contributed to multi-target tracking projects at Systems Planning and Analysis, Inc. from 2007 to 2015. In this role, she served as Program Manager and Senior Operations Research Analyst, and she developed modeling approaches for the U.S. Navy Submarine Security Program, Office of the Secretary of Defense (OSD), and Joint Program Offices, using stochastic processes, to understand various tactical problems in different domains; such as submarine search and detection in oceanographic and atmospheric environmental conditions for the Navy, and damage assessments and remediation of cyber attacks to the Defense Industrial Base for OSD. In addition, she spent two years as a Lecturer and Postdoctoral Researcher at the University of Maryland, College Park in Department of Mathematics from 2005 to 2007. She earned her Ph.D. in Applied and Computational Mathematics from Princeton University in 2005, where her research focused on developing and analyzingspatially-explicit stochastic models of deforestation in forest ecosystems of the Neotropics.
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