Use Model to Deconstruct Threats: Detect Intrusion by Statistical Learning


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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.
Participants
Tao Zhou

Participant

Senior Staff Algorithm Engineer, Alibaba Group


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