NODES Data Science Talk: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain

Data Science Nexus presents NODES a new Data Science Seminar Series.

Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain

Cuneyt Gurcan Akcora
Thursday, December 17, 2020
Time: 2:30 PM

Cuneyt Gurcan Akcora is an Assistant Professor of Computer Science and Statistics at the University of Manitoba, Canada. He received his Ph.D. from the University of Insubria, Italy. His primary research interests are Data Science on complex networks and large-scale graph analysis, with applications in social, biological, IoT and Blockchain networks. He is a Fulbright Scholarship recipient, and his research works have been published in leading conferences and journals including IEEEtran, VLDB, ICDM, IJCAI, and ICDE.
 

Abstract

The recent proliferation of cryptocurrencies that allow for pseudo-anonymous transactions has resulted in a spike of various e-crime activities and, particularly, cryptocurrency payments in hacking attacks demanding ransom by encrypting sensitive user data. Currently, most hackers use Bitcoin for payments, and existing ransomware detection tools depend only on a couple of heuristics and/or tedious data-gathering steps. By capitalizing on the recent advances in Topological Data Analysis, we propose a novel efficient and tractable framework to automatically predict new ransomware transactions in a ransomware family, given only limited records of past transactions. Moreover, our new methodology exhibits high utility to detect the emergence of new ransomware families, that is, detecting ransomware with no past records of transactions.
 

The talk including a live Q&A will be broadcast on the Faculty of Science YouTube channel.