Data Analytics for Cyber Security: Defeating the Active Adversaries
Abstract
The overall arching goal of this project is to develop techniques to increase the robustness of machine learning systems against adversarial attacks. In addition, we focus on understanding how the developed techniques could be leveraged in practice so that humans and ML tools could work together. Accomplishments: Please see the attached report and slides. Training Opportunities: We have organized a reading group on deep learning and adversarial machine learning topics allowing students to learn the recent advances in this area. In addition, Dr. Kantarcioglu thought a course on adversarial machine learning. The students who participated in this research had chance to learn cutting edge technologies with respect to deep learning, adversarial machine learning, IoT data, and explainable AI. Results Dissemination: The results are disseminated via publishing our work in major conferences and venues. In addition, PI Kantarcioglu and co-PIs Thurasingham and Xi gave many talks on the topic. Co-PI Thuraisingham participated in Women in Data Science event held Stanford University that was live streamed to around 100,000 people. She gave an overview of our Adversarial Machine Learning Research for ARO project.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 06, 2023
- Accession Number
- AD1222215
Entities
People
- Murat Kantarcıoğlu
Organizations
- University of Texas at Dallas