Search

Home > Data Skeptic > Fraud Detection with Graphs
Podcast: Data Skeptic
Episode:

Fraud Detection with Graphs
Category: Religion & Spirituality
Duration: 00:37:23
Publish Date: 2025-01-22 03:04:00
Description:

In this episode, Šimon Mandlík, a PhD candidate at the Czech Technical University will talk with us about leveraging machine learning and graph-based techniques for cybersecurity applications.

We'll learn how graphs are used to detect malicious activity in networks, such as identifying harmful domains and executable files by analyzing their relationships within vast datasets.

This will include the use of hierarchical multi-instance learning (HML) to represent JSON-based network activity as graphs and the advantages of analyzing connections between entities (like clients, domains etc.).

Our guest shows that while other graph methods (such as GNN or Label Propagation) lack in scalability or having trouble with heterogeneous graphs, his method can tackle them because of the "locality assumption" – fraud will be a local phenomenon in the graph – and by relying on this assumption, we can get faster and more accurate results.
Total Play: 0

Users also like

300+ Episodes
Good Law | B .. 80+     10+
400+ Episodes
The Knowledg .. 200+     10+
1K+ Episodes
Entrepreneur .. 600+     50+