Armin Sarabi

Research Scientist
University of Michigan

I am an Assistant Research Scientist in the Department of Electrical Engineering and Computer Science at the University of Michigan. I received my MSE and PhD in Electrical Engineering from the University of Michigan, and my BSc in Electrical Engineering from Sharif University of Technology in Iran.

My research is interdisciplinary and lies at the intersection of machine/deep learning and Internet security, spanning the areas of data-driven security, network measurement, and security economics. I am interested in developing intelligent frameworks for solving outstanding problems in Internet security and measurement. In particular, I leverage machine learning and statistical models in order to enable proactive security, and build automated monitoring tools that can keep up with the growing number of networked devices in the age of Internet of Things.

Publications

Conference Papers

  • Deterrence, Backup, or Insurance: A Game-Theoretic Analysis of Ransomware.
  • T. Yin, A. Sarabi and M. Liu.
  • In Workshop on the Economics of Information Security (WEIS), June 2021.
  • Characterizing the Internet Host Population Using Deep Learning: A Universal and Lightweight Numerical Embedding.
  • A. Sarabi and M. Liu.
  • In Internet Measurement Conference (IMC), pages 133–146, Boston, MA, Oct 2018.
  • From Patching Delays to Infection Symptoms: Using Risk Profiles for an Early Discovery of Vulnerabilities Exploited in the Wild.
  • C. Xiao, A. Sarabi, Y. Liu, B. Li, M. Liu, and T. Dumitraş.
  • In USENIX Security Symposium, pages 903–918, Baltimore, MD, Aug 2018.
  • Patch Me If You Can: A Study on the Effects of Individual User Behavior on the End-Host Vulnerability State.
  • A. Sarabi, Z. Zhu, C. Xiao, M. Liu, and T. Dumitraş.
  • In International Conference on Passive and Active Network Measurement (PAM), pages 113–125, Sydney, Australia, Mar 2017.
  • Prioritizing Security Spending: A Quantitative Analysis of Risk Distributions for Different Business Profiles.
  • A. Sarabi, P. Naghizadeh, Y. Liu, and M. Liu.
  • In Workshop on the Economics of Information Security (WEIS), Delft, The Netherlands, June 2015.
  • Cloudy with a Chance of Breach: Forecasting Cyber Security Incidents.
  • Y. Liu, A. Sarabi, J. Zhang, P. Naghizadeh, M. Karir, M. Bailey, and M. Liu.
  • In USENIX Security Symposium, pages 1009–1024, Washington, D.C., Aug 2015.
  • Predicting Cyber Security Incidents Using Feature-Based Characterization of Network-Level Malicious Activities.
  • Y. Liu, J. Zhang, A. Sarabi, M. Liu, M. Karir, and M. Bailey.
  • In ACM International Workshop on Security and Privacy Analytics (IWSPA), pages 3–9, San Antonio, TX, Mar 2015.

Journal Papers

  • Risky Business: Fine-Grained Data Breach Prediction Using Business Profiles.
  • A. Sarabi, P. Naghizadeh, Y. Liu, and M. Liu.
  • Journal of Cybersecurity, 2(1):15–28, Dec 2016.

Book Chapters

  • Smart Internet Probing: Scanning Using Adaptive Machine Learning.
  • A. Sarabi, K. Jin, and M. Liu.
  • Game Theory and Machine Learning for Cyber Security, pages 411–437, Wiley-IEEE Press, 2021.

Teaching

  • EECS 452: Digital Signal Processing Design Laboratory (Winter 2023).
  • Engineering 100: Self-Driving Cars, Drones, and Beyond: An Intro to Autonomous Electronic Systems (Fall 2018, Winter 2019).