RESUME

Svetlana Radosavac


Areas of Expertise | Education | Technical Skills | Experience | Selected Publications

§ Areas of Expertise

Cyber security, application fraud, data analysis, social Network Analysis (information and influence propagation, malware propagation), wireless networks, internet economy, game theory and behavioral economics.

§ Education

 2002-2007
 2000-2002
M.Sc. in Electrical and Computer Engineering
University of Maryland College Park
Thesis: Detection and Classification of Network Intrusions using Hidden Markov Models
Advisor: John S. Baras
 1999
B.Sc. in Electrical and Computer Engineering (5 year degree)
University of Belgrade, Serbia and Montenegro

§ Technical Skills

  • Python and Python scientific libraries (numpy, scipy, pandas, scikit-learn, etc.) (expert), Java (basic)
  • Machine Learning: scikit-learn, TensorFlow (basic), Keras (basic)
  • Big data tools: Spark (PySpark, Spark SQL), Hadoop
  • Graph analytic: Neo4j

§ Experience

Lead Analytic Scientist - Application Fraud, Fair Isaac Corporation (FICO), San Diego, CA

November 2017 - current

  • Application fraud: Part of the team that develops algorithms and methods for detecting application fraud. Responsible for developing machine learning models for fraud detection: building fraud detection variables, risk models, building data analysis pipeline using Python and Spark and exploring new modeling approaches for improving the existing models. Tools used: Java, Python, Spark

Analytic Scientist, Fair Isaac Corporation (FICO), San Diego, CA

September 2014 - November 2017

  • Cyber security: Part of the team that developed analytic model for detecting various levels of security threats, including DDoS attacks, beaconing, exfiltration and numerous other types of attacks. Analyzed large amounts of data to gather insights on normal and malicious behavior and reduce the number of false positives. Experience with analysis and threat detection in HTTP, DNS and NetFlow. Tools used: Java, Python, Spark, Hadoop. Relevant links: FICO Takes Fraud-Detection Techniques to Cybersecurity, FICO Cyber Security, FICO's CyberSecurity Analytics Solution to Detect and Preclude Malicious Network Activity.
  • Graph databeses for fraud detection: used Neo4j for detectiong credit card payment fraud, including detection of credit card testing sites. Used several months worth of credit card transaction data to create a model for detecting complex fraud networks. Tools used: Neo4j, Titan

Research Scientist,DOCOMO Innovations

  • Big Data Driven Content Distribution using SDN Approach
  • Security management for open mobile platforms
  • Incentive Engineering for Network Security in Collaboration with UC Berkley School of Information
  • Using insurance to increase internet security
  • Rebuilding the internet architecture: DDoS attacks and protection against them

Postdoctoral Research Assistant,Institute for Systems Research, College Park, MD

  • Secure Component Based routing (CBR) for MANETS
  • Impact of MAC layer misbehavior on the Network Layer

Graduate Research Assistant,Institute for Systems Research, College Park, MD

  • Resilient Cooperative Intrusion Detection Systems
  • Formal Models for Coordinated Attacks
  • Secure Component-Based Routing (CBR) for MANETs
  • Modeling and detecting access layer misbehavior in wireless networks
  • Max-entropy applications in detection of MAC layer misbehavior
  • Impact of MAC layer misbehavior on the Network Layer
  • Fast Innate and Adaptive Immune Systems

§ Publications

You can access my complete list of publications here

  1. S. Radosavac, U. Kozat and J. Kempf, “On the Use of Admission Control to prevent DDoS Attacks on Mobile Networks”, submitted to Transactions on Mobile Computing

  2. A. A. Cardenas, S. Radosavac and J. S. Baras, “Evaluation of Detection Algorithms for MAC Layer Misbehavior: Theory and Experiments”, IEEE/ACM Transactions on Networking (ToN), Pages 605-617, Vol. 17, Issue 2. April 2009.

  3. S. Radosavac, G. V. Moustakides, J. S. Baras and I. Koutsopoulos, “An analytic framework for modeling and detecting access layer misbehavior in wireless networks”, ACM Transactions on Information and System Security (ACM TISSEC), Vol. 11, No. 4, July 2008.

  4. S. Radosavac and J. S. Baras, “Application of Sequential Detection Schemes for Obtaining Performance Bounds of Greedy Users in the IEEE 802.11 MAC”, IEEE Communications Magazine: special issue on Security in Mobile Ad Hoc and Sensor Networks, pages 148-154, Vol. 46, No. 2, February 2008.

  5. H. Sharara, C. Westphal, S. Radosavac and U. C. Kozat, “Utilizing Social Influence in Content Distribution Networks” in Proceedings of IEEE ICC-2011, Kyoto, Japan, 2011 (best paper award)

  6. J. Grossklags, S. Radosavac, A. Cardenas, J. Chuang, “Nudge: Intermediaries’ Role in Interdependent Network Security” Proceedings of 3rd International Conference on Trust and Trustworthy Computing (Trust’10), June 2010.

  7. A. Cardenas, S. Radosavac, J. Grossklags, J. Chuang and C. Hoofnagle, “An Economic Map of Cybercrime”, 37th Research Conference on Communication, Information and Internet Policy (TPRC) 2009, George Mason University Law School, Arlington, VA, September 25-27, 2009.

  8. S. Radosavac, J. Kempf and U. C. Kozat , “Using Insurance for Increasing Internet Security”, ACM SIGCOMM Workshop on the Economics of Networks, Systems and Computation (NetEcon ’08), August 22, Seattle, WA

  9. A. A. Cardenas, S. Radosavac and J. S. Baras, “An Analytical Evaluation of MAC Layer Misbehavior Detection Schemes”, Proceedings of the 26th Annual IEEE Conference on Computer Communications, INFOCOM 2007.

Areas of Expertise | Education | Technical Skills | Experience | Selected Publications