Data Mining for Networks: Algorithms and Architecture
Lecturers
Description:
This course consists a first part where we will take a deep dive into the middlewares available for performing data mining at large scale, e.g., Hadoop/Spark for bach processing.
Then, in a second part, we will explore how to use classic methods from data mining in a networking context. We will first present some methods among K-means, Support Vector Machines, Principal Component Analysis, ... We will then show some applications of these methods to solve important networking problems such as the detection of anomalies in network traffic to detect potential threats or discover the source of failures or the discovery of clusters in social networks.
Description:
This course consists a first part where we will take a deep dive into the middlewares available for performing data mining at large scale, e.g., Hadoop/Spark for bach processing.
Then, in a second part, we will explore how to use classic methods from data mining in a networking context. We will first present some methods among K-means, Support Vector Machines, Principal Component Analysis, ... We will then show some applications of these methods to solve important networking problems such as the detection of anomalies in network traffic to detect potential threats or discover the source of failures or the discovery of clusters in social networks.