In the new security landscape, blind spots in network traffic can not solely be monitored by security tools designed for simple, on-premise traditional architectures.
Modern organizations are implementing a combination of machine learning, advanced analytics, and rule-based detection to detect suspicious activities on enterprise networks.
In this presentation we'll go through three uses cases where machine learning can be applied in network traffic analysis:
*Detecting Credential Misuse using Lateral Movement
*Identify Credential Stuffing Attack using Behavioral Modeling
*C2 (Command & Control) Detection using Relationship Based Modeling