User Behavior Analysis with Splunk: Detecting Threats and Fraudulent Activity in the Ocean of Behaviors: Part 2 – Detecting Abnormal User Session Velocity and Density
One of my enterprise clients observed that certain class of attacks having a number of distinctive characteristics: attacker who possessed correct user account credentials won’t try to engage into malicious behavior right away.
Initial activity would involve certain degree of reconnaissance and gathering of future victim’s specific data, such as account balances, trade histories and other. So normal “red flags” and risk scoring metrics won’t generate any alerts.
However in many cases such pre-fraudulent activity was still carrying an unusual behavior marks: either session velocity (average number of seconds per hit) or session density (number of hits within the session) or both exceeded normal, baseline session patterns typical for the average client application user’s behavior.
Abnormally high session velocity is also a typical pattern of an automated script-driven session that both fraudsters and competition were using to siphon data from the client’s servers.
One of the possible solutions to detect these activities would be to calculate average session velocity and density and then apply these values to trigger alerts when session metrics exceeded thresholds.
The issue here is that due to the client’s business specific these averages vary greatly depending on the time of the day, time of the week and also the month of the year.
So stuffing some fixed “guessed” threshold values won’t work and will either generate lots of false positives or miss many suspicious sessions. […]