Alina Oprea

Security analytics
Cloud security
Network security
Applied cryptography

PhD in Computer Science, Carnegie Mellon University
MS in Computer Science, Carnegie Mellon University
BS in Mathematics and Computer Science, University of Bucharest — Romania

Alina Oprea is a professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston.

Oprea is interested in extracting meaningful intelligence from different data sources for security applications. By designing rigorous machine learning techniques to predict the behavior of sophisticated attackers, she hopes to protect cloud infrastructures against emerging threats. Oprea co-directs the Network and Distributed Systems Security Lab, which focuses on building distributed systems and network protocols that achieve security, availability, and performance.

Before joining Khoury College, Oprea was a research scientist at RSA Laboratories, where she studied cloud security, applied cryptography, foundations of cybersecurity, and security analytics.

As the co-author of numerous journal and peer-review conference papers, Oprea has participated in many technical program committees — including IEEE S&P, NDSS, ACM CCS, ACSAC, and DSN — and is a co-inventor on 20 patents. She is an associate editor for the ACM Transactions on Privacy and Security journal. At the 2005 Network and Distributed System Security Conference, Oprea earned the Best Paper Award, and in 2011, she received the Technology Review TR35 award for her research in cloud security.

Published: September 1st, 2017
Lens on the endpoint: Hunting for malicious software through endpoint data analysis

Citation: Ahmet Salih Buyukkayhan, Alina Oprea, Zhou Li, William Robertson. Lens on the endpoint: Hunting for malicious software through endpoint data analysis. International Symposium on Research in Attacks, Intrusions and Defenses (RAID). September 2017.
Published: May 27th, 2018
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

Citation: Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning Matthew Jagielski, Alina Oprea, Chang Liu, Cristina Nita-Rotaru, and Bo Li IEEE S&P (Oakland) 2018
Published: December 6th, 2018
Differentially Private Fair Learning

Citation: Jagielski, Matthew, Kearns, Michael, Mao, Jieming, Oprea, Alina, Roth, Aaron, Sharifi, Saeed, & Ullman, Jonathan. (2019). Differentially Private Fair Learning. Proceedings of the 36 Th International Conference on Machine Learning.