A multi-level intrusion detection method for abnormal network behaviors.

Abnormal network traffic analysis has become an increasingly important research topic to protect computing infrastructures from intruders. Yet, it is challenging to accurately discover threats due to the high volume of network traffic. To have better knowledge about network intrusions, this paper focuses on designing a multi-level network detection method. Mainly, it is composed of three steps as (1) understanding hidden underlying patterns from network traffic data by creating reliable rules to identify network abnormality, (2) generating a predictive model to determine exact attack categories, and (3) integrating a visual analytics tool to conduct an interactive visual analysis and validate the identified intrusions with transparent reasons.