DBSCAN for Large-Scale Datasets and Network Security Applications - Avidan Avraham and Asaf Fried

You've probably heard of DBSCAN (Density-Based Spatial Clustering of Applications with Noise), as it is one of the most well-known density-based clustering algorithms. Since it was first introduced in 1996, this field has been extensively studied in academia and successfully applied to many real-world industry applications.
However, due to its high computational complexity, applying DBSCAN to today's large-scale data sets is very challenging. In this talk, you will learn how DBSCAN can be parallelized and executed over distributed processing systems using PySpark. In the end, we will show how we apply these methods to solve network security problems here at Cato.
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Avidan Avraham, Research Team Lead, Cato Networks
Avidan Avraham is the Research Team Lead at Cato Networks, with more than 10 years of industry experience in both Network and Endpoint Security. As a research lead, Avidan applies novel methods for detection and prevention of security threats using ML and other data driven approaches. Avidan is also a data-engineering geek, focused on ways to allow fast and efficient access to data for research.
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Asaf Fried, Data Scientist, Cato Networks
Asaf is a Data Scientist in Cato’s Research Labs at Cato Networks. Asaf has more than five years of both academic and industry experience in applying state-of-the-art machine learning methods to the domain of cybersecurity. His main research interests are social engineering and network-based attacks in enterprise environments.
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