Use Model to Deconstruct Threats: Detect Intrusion by Statistical Learning


Posted on in Presentations

Machine learning has been widely discussed in various areas. However, there is not much discussion about intrusion detection in large scale enterprise networks. This talk will propose a method based on statistical learning. The main idea is to identify unknown threats by modeling behaviors at different attack stages, and some tricks in performing pre-filter data and conducting post-correlate alarms.

Learning Objectives:
1: Understand the basic information of the security operations in a large Internet company.
2: Learn how to use statistical models to identify the unique patterns of post-exploitation attacks.
3: Master the necessary skills in performing pre-filter data and conducting post-correlate alarms.

Pre-Requisites:
Have basic statistical knowledge, familiar with the popular attack techniques.

Analytics Intelligence & Response

security analytics network security intrusion prevention/detection big data analytics


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