Sara Khanchi

Sara Khanchi

Title: Assistant Professor

Department: College of Engineering & Computing Sciences

Campus: Vancouver

Area(s) of Expertise: Computer Networks, Network Security, Intrusion Detection, Streaming Classification, Malware Analysis, Network Traffic Analysis

Education Credentials: Ph.D.

Joined New York Tech: 2021


Sara Khanchi is an assistant professor of computer science at New York Tech's Vancouver campus. She received her Ph.D. in computer science from Dalhousie University in Halifax, Canada in 2019. She has several years of experience in industry, mainly in areas of malware analysis and development of secure network devices and services. Her current research interests are in the intersection of intrusion detection systems (IDS), malware analysis, machine learning, and network traffic analysis.

Selected Publications

  1. S. Khanchi, A. Vahdat, M. I. Heywood, A. N. Zincir-Heywood, On botnet detection with genetic programming under streaming data label budgets and class imbalance, Swarm and Evolutionary Computation, Volume 39, 2018.
  2. S. Khanchi, M. I. Heywood, A. N. Zincir-Heywood, Network Analytics for Streaming Traffic Analysis, IFIP/IEEE International Symposium on Integrated Network Management, IM 2019, Washington, DC, USA, April, 2019.
  3. S. Khanchi, A. Vahdat, M. I. Heywood, A. N. Zincir-Heywood, On botnet detection with genetic programming under streaming data label budgets and class imbalance, Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO, Kyoto, Japan, July 2018.
  4. D. C. Le, S. Khanchi, A. N. Zincir-Heywood, M. I. Heywood, Benchmarking evolutionary computation approaches to insider threat detection, Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO, Kyoto, Japan, July 2018.
  5. S. Khanchi, A. N. Zincir-Heywood, M. I. Heywood, Streaming Botnet traffic analysis using bio-inspired active learning, IEEE/IFIP Network Operations and Management Symposium, NOMS, Taipei, Taiwan, April, 2018.
  6. S. Khanchi, M. Heywood, and N. Zincir-Heywood, Properties of a GP Active Learning Framework for Streaming Data with Class Imbalance, GECCO, July. 2017.
  7. S. Khanchi, M. Heywood, and N. Zincir-Heywood, On the Impact of Class Imbalance in GP Streaming Classification with Label Budgets, EuroGP, Apr. 2016.

Professional Honors and Awards

Courses Taught at New York Tech

Contact Info

X

By continuing to use the website, you consent to analytics tracking per NYIT's Privacy Statement