Intel ISTC-ARSA Center at Georgia Tech


The Intel Science & Technology Center for Adversary-Resilient Security Analytics (ISTC-ARSA) at the Georgia Institute of Technology is dedicated to the emerging field of machine-learning (ML) cybersecurity. Researchers from Intel Corporation, along with students and faculty from Georgia Tech, are strengthening the analytics behind malware detection and threat analysis.

Researchers study the vulnerabilities of ML algorithms and develop new security approaches to improve the resilience of ML applications. Outcomes are expected to benefit the security of analytics, search engines, customized news feeds, facial and voice recognition, fraud detection, and more.

Intel Corp. researchers

Li Chen is the co-primary investigator (PI) and research lead at the Intel Science & Technology Center for Adversary-Resilient Security Analytics. She designs the roadmaps with Intel and Georgia Tech PIs to jointly meet both industrial and academic research objectives. She also provides research direction and in-depth technical guidance to advance the ARSA research agenda. She is a data scientist and research scientist in the Security and Privacy Lab at Intel Labs, where she focuses on developing state-of-the-art robust machine learning and deep learning algorithms for security analytics including applications in malware detection and image classification in the adversarial setting. Prior to joining Intel Labs, Chen was a Data Scientist in Software and Services Group at Intel, where she focused on developing advanced and principled machine learning methods for cloud workload characterization and cloud computing performance. Li Chen received her Ph.D. degree in Applied Mathematics and Statistics from Johns Hopkins University. Her research interests primarily include machine learning, statistical pattern recognition, computational statistics, random graph inference, data mining, and inference for high-dimensional data. Her research has been featured in a number of pioneering scientific and engineering journals and conferences including IEEE Transactions on Pattern Analysis and Machine Intelligence, Annals of Applied Statistics, Parallel Computing, AAAI Conference on Artificial Intelligence and SPIE. She has given more than 30 technical presentations, including at the Joint Statistical Meeting (the largest statistics conference in North America), AAAI conference, International Joint Conference on Artificial Intelligence, and Spring Research Conference on Statistics and Industry Technology.

Michael Kounavis is a research scientist with Intel Corporation, working in the areas of machine learning, computer vision and cryptography. His current research focuses on adversarial machine learning. Dr. Kounvavis co-invented Intel’s AES-NI instruction set for accelerating AES encryption, an accomplishment for which he received an Intel Achievement Award in 2008, and is one of the main authors of Intel’s intellectual property portfolio in the area of hand gesture recognition. He has published more than 60 technical papers in the above areas and holds more than 20 patents. His prior work includes an early proposal on  programmable virtual networks (Spawning Networks, 1999), which was a forerunner of today’s Software Defined Network (SDN) architectures. This work eventually became Kounvavis' Ph.D. thesis, which was awarded with distinction from Columbia University in 2004. 

Scott Buck is a member of the University Research Collaborative Group in Intel Labs where he is the Program Director of the Intel Science and Technology Centers (ISTC) for Adversarial-Resilient Security Analytics, Secure Computing, Cyber Physical Systems and Intel’s AI Academic Outreach Research Program.  Scott joined Intel in 1995 and has more than 30 years of experience in high technology.

Georgia Tech researchers

Wenke Lee is the John P. Imlay Jr. chair of software in the College of Computing and co-director of the Institute for Information Security & Privacy (IISP), at the Georgia Institute of Technology. Lee’s research interests are systems and network security, applied cryptography, and data mining. Lee has researched extensively in intrusion and botnet detection and malware analysis, and has pioneered research in applying machine-learning techniques to security analysis problems as well as conducted research in adversarial machine learning.

Polo Chau, assistant professor, received his Ph.D. in Machine Learning from Carnegie Mellon University in 2012. His research interests are machine learning, security analytics including malware analysis, and human-computer interaction. Chau will lead the development of countermeasures, in particular, the ensemble framework.

Taesoo Kim, assistant professor, received his Ph.D. in Computer Science from Massachusetts Institute of Technology in 2014. Kim’s research interests are systems security, malware analysis, and security analytics. He will lead the development of the MLsploit toolkit and also will incorporate results from this project into other curriculum development efforts funded by Intel and the National Science Foundation.

Le Song, assistant professor, received his Ph.D. in Computer Science from the University of Sydney in 2008. His research interests are machine learning and its applications. Song will lead the theoretical studies of machine learning vulnerabilities and adversaries’ capabilities, as well as algorithmic improvements to machine learning. 

The research is supported by Intel Corp. through a grant to the Georgia Tech Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsoring agency. Intel is a registered trademark of Intel Corporation in the United States and other countries.